Random Forest Construction With Robust Semisupervised Node Splitting
Random forest (RF) is a very important classifier with applications in various machine learning tasks, but its promising performance heavily relies on the size of labeled training data. In this paper, we investigate constructing of RFs with a small size of labeled data and find that the performance...
Ausführliche Beschreibung
Autor*in: |
Xiao Liu [verfasserIn] |
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Artikel |
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Sprache: |
Englisch |
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2015 |
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Übergeordnetes Werk: |
Enthalten in: IEEE transactions on image processing - New York, NY : Inst., 1992, 24(2015), 1, Seite 471-483 |
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Übergeordnetes Werk: |
volume:24 ; year:2015 ; number:1 ; pages:471-483 |
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DOI / URN: |
10.1109/TIP.2014.2378017 |
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Katalog-ID: |
OLC1959239910 |
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520 | |a Random forest (RF) is a very important classifier with applications in various machine learning tasks, but its promising performance heavily relies on the size of labeled training data. In this paper, we investigate constructing of RFs with a small size of labeled data and find that the performance bottleneck is located in the node splitting procedures; hence, existing solutions fail to properly partition the feature space if there are insufficient training data. To achieve robust node splitting with insufficient data, we present semisupervised splitting to overcome this limitation by splitting nodes with the guidance of both labeled and abundant unlabeled data. In particular, an accurate quality measure of node splitting is obtained by carrying out the kernel-based density estimation, whereby a multiclass version of asymptotic mean integrated squared error criterion is proposed to adaptively select the optimal bandwidth of the kernel. To avoid the curse of dimensionality, we project the data points from the original high-dimensional feature space onto a low-dimensional subspace before estimation. A unified optimization framework is proposed to select a coupled pair of subspace and separating hyperplane such that the smoothness of the subspace and the quality of the splitting are guaranteed simultaneously. Our algorithm efficiently avoids overfitting caused by bad initialization and local maxima when compared with conventional margin maximization-based semisupervised methods. We demonstrate the effectiveness of the proposed algorithm by comparing it with state-of-the-art supervised and semisupervised algorithms for typical computer vision applications, such as object categorization, face recognition, and image segmentation, on publicly available data sets. | ||
650 | 4 | |a low-dimensional subspace | |
650 | 4 | |a learning (artificial intelligence) | |
650 | 4 | |a Covariance matrices | |
650 | 4 | |a semi-supervised splitting | |
650 | 4 | |a local maxima | |
650 | 4 | |a Estimation | |
650 | 4 | |a semi-supervised learning | |
650 | 4 | |a random forest | |
650 | 4 | |a random forest construction | |
650 | 4 | |a hyperplane separation | |
650 | 4 | |a kernel-based density estimation | |
650 | 4 | |a robust semisupervised node splitting | |
650 | 4 | |a labeled training data | |
650 | 4 | |a Training data | |
650 | 4 | |a unified optimization framework | |
650 | 4 | |a Kernel | |
650 | 4 | |a data points | |
650 | 4 | |a asymptotic mean integrated squared error criterion | |
650 | 4 | |a image classification | |
650 | 4 | |a Bandwidth | |
650 | 4 | |a computer vision | |
650 | 4 | |a Training | |
650 | 4 | |a supervised algorithms | |
650 | 4 | |a high-dimensional feature space | |
650 | 4 | |a Radio frequency | |
650 | 4 | |a subspace learning | |
650 | 4 | |a machine learning tasks | |
650 | 4 | |a quality measure | |
650 | 4 | |a margin maximization-based semisupervised methods | |
650 | 4 | |a node splitting | |
650 | 4 | |a optimisation | |
650 | 4 | |a Feature extraction | |
650 | 4 | |a Face - anatomy & histology | |
650 | 4 | |a Image Processing, Computer-Assisted - methods | |
650 | 4 | |a Pattern Recognition, Automated - methods | |
700 | 0 | |a Mingli Song |4 oth | |
700 | 0 | |a Dacheng Tao |4 oth | |
700 | 0 | |a Zicheng Liu |4 oth | |
700 | 0 | |a Luming Zhang |4 oth | |
700 | 0 | |a Chun Chen |4 oth | |
700 | 0 | |a Jiajun Bu |4 oth | |
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856 | 4 | 2 | |u http://www.ncbi.nlm.nih.gov/pubmed/25494503 |
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10.1109/TIP.2014.2378017 doi PQ20160617 (DE-627)OLC1959239910 (DE-599)GBVOLC1959239910 (PRQ)c2625-33afd5198206abf0a23259fccd5db890d7a414e7e5caf12f44ca42b8d094e2120 (KEY)0213811520150000024000100471randomforestconstructionwithrobustsemisupervisedno DE-627 ger DE-627 rakwb eng 004 620 DNB 54.00 bkl Xiao Liu verfasserin aut Random Forest Construction With Robust Semisupervised Node Splitting 2015 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier Random forest (RF) is a very important classifier with applications in various machine learning tasks, but its promising performance heavily relies on the size of labeled training data. In this paper, we investigate constructing of RFs with a small size of labeled data and find that the performance bottleneck is located in the node splitting procedures; hence, existing solutions fail to properly partition the feature space if there are insufficient training data. To achieve robust node splitting with insufficient data, we present semisupervised splitting to overcome this limitation by splitting nodes with the guidance of both labeled and abundant unlabeled data. In particular, an accurate quality measure of node splitting is obtained by carrying out the kernel-based density estimation, whereby a multiclass version of asymptotic mean integrated squared error criterion is proposed to adaptively select the optimal bandwidth of the kernel. To avoid the curse of dimensionality, we project the data points from the original high-dimensional feature space onto a low-dimensional subspace before estimation. A unified optimization framework is proposed to select a coupled pair of subspace and separating hyperplane such that the smoothness of the subspace and the quality of the splitting are guaranteed simultaneously. Our algorithm efficiently avoids overfitting caused by bad initialization and local maxima when compared with conventional margin maximization-based semisupervised methods. We demonstrate the effectiveness of the proposed algorithm by comparing it with state-of-the-art supervised and semisupervised algorithms for typical computer vision applications, such as object categorization, face recognition, and image segmentation, on publicly available data sets. low-dimensional subspace learning (artificial intelligence) Covariance matrices semi-supervised splitting local maxima Estimation semi-supervised learning random forest random forest construction hyperplane separation kernel-based density estimation robust semisupervised node splitting labeled training data Training data unified optimization framework Kernel data points asymptotic mean integrated squared error criterion image classification Bandwidth computer vision Training supervised algorithms high-dimensional feature space Radio frequency subspace learning machine learning tasks quality measure margin maximization-based semisupervised methods node splitting optimisation Feature extraction Face - anatomy & histology Image Processing, Computer-Assisted - methods Pattern Recognition, Automated - methods Mingli Song oth Dacheng Tao oth Zicheng Liu oth Luming Zhang oth Chun Chen oth Jiajun Bu oth Enthalten in IEEE transactions on image processing New York, NY : Inst., 1992 24(2015), 1, Seite 471-483 (DE-627)131074458 (DE-600)1111265-7 (DE-576)029165008 1057-7149 nnns volume:24 year:2015 number:1 pages:471-483 http://dx.doi.org/10.1109/TIP.2014.2378017 Volltext http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=6975199 http://www.ncbi.nlm.nih.gov/pubmed/25494503 http://search.proquest.com/docview/1640798292 GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC SSG-OLC-MAT GBV_ILN_70 GBV_ILN_2005 GBV_ILN_4318 54.00 AVZ AR 24 2015 1 471-483 |
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10.1109/TIP.2014.2378017 doi PQ20160617 (DE-627)OLC1959239910 (DE-599)GBVOLC1959239910 (PRQ)c2625-33afd5198206abf0a23259fccd5db890d7a414e7e5caf12f44ca42b8d094e2120 (KEY)0213811520150000024000100471randomforestconstructionwithrobustsemisupervisedno DE-627 ger DE-627 rakwb eng 004 620 DNB 54.00 bkl Xiao Liu verfasserin aut Random Forest Construction With Robust Semisupervised Node Splitting 2015 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier Random forest (RF) is a very important classifier with applications in various machine learning tasks, but its promising performance heavily relies on the size of labeled training data. In this paper, we investigate constructing of RFs with a small size of labeled data and find that the performance bottleneck is located in the node splitting procedures; hence, existing solutions fail to properly partition the feature space if there are insufficient training data. To achieve robust node splitting with insufficient data, we present semisupervised splitting to overcome this limitation by splitting nodes with the guidance of both labeled and abundant unlabeled data. In particular, an accurate quality measure of node splitting is obtained by carrying out the kernel-based density estimation, whereby a multiclass version of asymptotic mean integrated squared error criterion is proposed to adaptively select the optimal bandwidth of the kernel. To avoid the curse of dimensionality, we project the data points from the original high-dimensional feature space onto a low-dimensional subspace before estimation. A unified optimization framework is proposed to select a coupled pair of subspace and separating hyperplane such that the smoothness of the subspace and the quality of the splitting are guaranteed simultaneously. Our algorithm efficiently avoids overfitting caused by bad initialization and local maxima when compared with conventional margin maximization-based semisupervised methods. We demonstrate the effectiveness of the proposed algorithm by comparing it with state-of-the-art supervised and semisupervised algorithms for typical computer vision applications, such as object categorization, face recognition, and image segmentation, on publicly available data sets. low-dimensional subspace learning (artificial intelligence) Covariance matrices semi-supervised splitting local maxima Estimation semi-supervised learning random forest random forest construction hyperplane separation kernel-based density estimation robust semisupervised node splitting labeled training data Training data unified optimization framework Kernel data points asymptotic mean integrated squared error criterion image classification Bandwidth computer vision Training supervised algorithms high-dimensional feature space Radio frequency subspace learning machine learning tasks quality measure margin maximization-based semisupervised methods node splitting optimisation Feature extraction Face - anatomy & histology Image Processing, Computer-Assisted - methods Pattern Recognition, Automated - methods Mingli Song oth Dacheng Tao oth Zicheng Liu oth Luming Zhang oth Chun Chen oth Jiajun Bu oth Enthalten in IEEE transactions on image processing New York, NY : Inst., 1992 24(2015), 1, Seite 471-483 (DE-627)131074458 (DE-600)1111265-7 (DE-576)029165008 1057-7149 nnns volume:24 year:2015 number:1 pages:471-483 http://dx.doi.org/10.1109/TIP.2014.2378017 Volltext http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=6975199 http://www.ncbi.nlm.nih.gov/pubmed/25494503 http://search.proquest.com/docview/1640798292 GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC SSG-OLC-MAT GBV_ILN_70 GBV_ILN_2005 GBV_ILN_4318 54.00 AVZ AR 24 2015 1 471-483 |
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10.1109/TIP.2014.2378017 doi PQ20160617 (DE-627)OLC1959239910 (DE-599)GBVOLC1959239910 (PRQ)c2625-33afd5198206abf0a23259fccd5db890d7a414e7e5caf12f44ca42b8d094e2120 (KEY)0213811520150000024000100471randomforestconstructionwithrobustsemisupervisedno DE-627 ger DE-627 rakwb eng 004 620 DNB 54.00 bkl Xiao Liu verfasserin aut Random Forest Construction With Robust Semisupervised Node Splitting 2015 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier Random forest (RF) is a very important classifier with applications in various machine learning tasks, but its promising performance heavily relies on the size of labeled training data. In this paper, we investigate constructing of RFs with a small size of labeled data and find that the performance bottleneck is located in the node splitting procedures; hence, existing solutions fail to properly partition the feature space if there are insufficient training data. To achieve robust node splitting with insufficient data, we present semisupervised splitting to overcome this limitation by splitting nodes with the guidance of both labeled and abundant unlabeled data. In particular, an accurate quality measure of node splitting is obtained by carrying out the kernel-based density estimation, whereby a multiclass version of asymptotic mean integrated squared error criterion is proposed to adaptively select the optimal bandwidth of the kernel. To avoid the curse of dimensionality, we project the data points from the original high-dimensional feature space onto a low-dimensional subspace before estimation. A unified optimization framework is proposed to select a coupled pair of subspace and separating hyperplane such that the smoothness of the subspace and the quality of the splitting are guaranteed simultaneously. Our algorithm efficiently avoids overfitting caused by bad initialization and local maxima when compared with conventional margin maximization-based semisupervised methods. We demonstrate the effectiveness of the proposed algorithm by comparing it with state-of-the-art supervised and semisupervised algorithms for typical computer vision applications, such as object categorization, face recognition, and image segmentation, on publicly available data sets. low-dimensional subspace learning (artificial intelligence) Covariance matrices semi-supervised splitting local maxima Estimation semi-supervised learning random forest random forest construction hyperplane separation kernel-based density estimation robust semisupervised node splitting labeled training data Training data unified optimization framework Kernel data points asymptotic mean integrated squared error criterion image classification Bandwidth computer vision Training supervised algorithms high-dimensional feature space Radio frequency subspace learning machine learning tasks quality measure margin maximization-based semisupervised methods node splitting optimisation Feature extraction Face - anatomy & histology Image Processing, Computer-Assisted - methods Pattern Recognition, Automated - methods Mingli Song oth Dacheng Tao oth Zicheng Liu oth Luming Zhang oth Chun Chen oth Jiajun Bu oth Enthalten in IEEE transactions on image processing New York, NY : Inst., 1992 24(2015), 1, Seite 471-483 (DE-627)131074458 (DE-600)1111265-7 (DE-576)029165008 1057-7149 nnns volume:24 year:2015 number:1 pages:471-483 http://dx.doi.org/10.1109/TIP.2014.2378017 Volltext http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=6975199 http://www.ncbi.nlm.nih.gov/pubmed/25494503 http://search.proquest.com/docview/1640798292 GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC SSG-OLC-MAT GBV_ILN_70 GBV_ILN_2005 GBV_ILN_4318 54.00 AVZ AR 24 2015 1 471-483 |
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10.1109/TIP.2014.2378017 doi PQ20160617 (DE-627)OLC1959239910 (DE-599)GBVOLC1959239910 (PRQ)c2625-33afd5198206abf0a23259fccd5db890d7a414e7e5caf12f44ca42b8d094e2120 (KEY)0213811520150000024000100471randomforestconstructionwithrobustsemisupervisedno DE-627 ger DE-627 rakwb eng 004 620 DNB 54.00 bkl Xiao Liu verfasserin aut Random Forest Construction With Robust Semisupervised Node Splitting 2015 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier Random forest (RF) is a very important classifier with applications in various machine learning tasks, but its promising performance heavily relies on the size of labeled training data. In this paper, we investigate constructing of RFs with a small size of labeled data and find that the performance bottleneck is located in the node splitting procedures; hence, existing solutions fail to properly partition the feature space if there are insufficient training data. To achieve robust node splitting with insufficient data, we present semisupervised splitting to overcome this limitation by splitting nodes with the guidance of both labeled and abundant unlabeled data. In particular, an accurate quality measure of node splitting is obtained by carrying out the kernel-based density estimation, whereby a multiclass version of asymptotic mean integrated squared error criterion is proposed to adaptively select the optimal bandwidth of the kernel. To avoid the curse of dimensionality, we project the data points from the original high-dimensional feature space onto a low-dimensional subspace before estimation. A unified optimization framework is proposed to select a coupled pair of subspace and separating hyperplane such that the smoothness of the subspace and the quality of the splitting are guaranteed simultaneously. Our algorithm efficiently avoids overfitting caused by bad initialization and local maxima when compared with conventional margin maximization-based semisupervised methods. We demonstrate the effectiveness of the proposed algorithm by comparing it with state-of-the-art supervised and semisupervised algorithms for typical computer vision applications, such as object categorization, face recognition, and image segmentation, on publicly available data sets. low-dimensional subspace learning (artificial intelligence) Covariance matrices semi-supervised splitting local maxima Estimation semi-supervised learning random forest random forest construction hyperplane separation kernel-based density estimation robust semisupervised node splitting labeled training data Training data unified optimization framework Kernel data points asymptotic mean integrated squared error criterion image classification Bandwidth computer vision Training supervised algorithms high-dimensional feature space Radio frequency subspace learning machine learning tasks quality measure margin maximization-based semisupervised methods node splitting optimisation Feature extraction Face - anatomy & histology Image Processing, Computer-Assisted - methods Pattern Recognition, Automated - methods Mingli Song oth Dacheng Tao oth Zicheng Liu oth Luming Zhang oth Chun Chen oth Jiajun Bu oth Enthalten in IEEE transactions on image processing New York, NY : Inst., 1992 24(2015), 1, Seite 471-483 (DE-627)131074458 (DE-600)1111265-7 (DE-576)029165008 1057-7149 nnns volume:24 year:2015 number:1 pages:471-483 http://dx.doi.org/10.1109/TIP.2014.2378017 Volltext http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=6975199 http://www.ncbi.nlm.nih.gov/pubmed/25494503 http://search.proquest.com/docview/1640798292 GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC SSG-OLC-MAT GBV_ILN_70 GBV_ILN_2005 GBV_ILN_4318 54.00 AVZ AR 24 2015 1 471-483 |
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10.1109/TIP.2014.2378017 doi PQ20160617 (DE-627)OLC1959239910 (DE-599)GBVOLC1959239910 (PRQ)c2625-33afd5198206abf0a23259fccd5db890d7a414e7e5caf12f44ca42b8d094e2120 (KEY)0213811520150000024000100471randomforestconstructionwithrobustsemisupervisedno DE-627 ger DE-627 rakwb eng 004 620 DNB 54.00 bkl Xiao Liu verfasserin aut Random Forest Construction With Robust Semisupervised Node Splitting 2015 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier Random forest (RF) is a very important classifier with applications in various machine learning tasks, but its promising performance heavily relies on the size of labeled training data. In this paper, we investigate constructing of RFs with a small size of labeled data and find that the performance bottleneck is located in the node splitting procedures; hence, existing solutions fail to properly partition the feature space if there are insufficient training data. To achieve robust node splitting with insufficient data, we present semisupervised splitting to overcome this limitation by splitting nodes with the guidance of both labeled and abundant unlabeled data. In particular, an accurate quality measure of node splitting is obtained by carrying out the kernel-based density estimation, whereby a multiclass version of asymptotic mean integrated squared error criterion is proposed to adaptively select the optimal bandwidth of the kernel. To avoid the curse of dimensionality, we project the data points from the original high-dimensional feature space onto a low-dimensional subspace before estimation. A unified optimization framework is proposed to select a coupled pair of subspace and separating hyperplane such that the smoothness of the subspace and the quality of the splitting are guaranteed simultaneously. Our algorithm efficiently avoids overfitting caused by bad initialization and local maxima when compared with conventional margin maximization-based semisupervised methods. We demonstrate the effectiveness of the proposed algorithm by comparing it with state-of-the-art supervised and semisupervised algorithms for typical computer vision applications, such as object categorization, face recognition, and image segmentation, on publicly available data sets. low-dimensional subspace learning (artificial intelligence) Covariance matrices semi-supervised splitting local maxima Estimation semi-supervised learning random forest random forest construction hyperplane separation kernel-based density estimation robust semisupervised node splitting labeled training data Training data unified optimization framework Kernel data points asymptotic mean integrated squared error criterion image classification Bandwidth computer vision Training supervised algorithms high-dimensional feature space Radio frequency subspace learning machine learning tasks quality measure margin maximization-based semisupervised methods node splitting optimisation Feature extraction Face - anatomy & histology Image Processing, Computer-Assisted - methods Pattern Recognition, Automated - methods Mingli Song oth Dacheng Tao oth Zicheng Liu oth Luming Zhang oth Chun Chen oth Jiajun Bu oth Enthalten in IEEE transactions on image processing New York, NY : Inst., 1992 24(2015), 1, Seite 471-483 (DE-627)131074458 (DE-600)1111265-7 (DE-576)029165008 1057-7149 nnns volume:24 year:2015 number:1 pages:471-483 http://dx.doi.org/10.1109/TIP.2014.2378017 Volltext http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=6975199 http://www.ncbi.nlm.nih.gov/pubmed/25494503 http://search.proquest.com/docview/1640798292 GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC SSG-OLC-MAT GBV_ILN_70 GBV_ILN_2005 GBV_ILN_4318 54.00 AVZ AR 24 2015 1 471-483 |
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low-dimensional subspace learning (artificial intelligence) Covariance matrices semi-supervised splitting local maxima Estimation semi-supervised learning random forest random forest construction hyperplane separation kernel-based density estimation robust semisupervised node splitting labeled training data Training data unified optimization framework Kernel data points asymptotic mean integrated squared error criterion image classification Bandwidth computer vision Training supervised algorithms high-dimensional feature space Radio frequency subspace learning machine learning tasks quality measure margin maximization-based semisupervised methods node splitting optimisation Feature extraction Face - anatomy & histology Image Processing, Computer-Assisted - methods Pattern Recognition, Automated - methods |
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Xiao Liu @@aut@@ Mingli Song @@oth@@ Dacheng Tao @@oth@@ Zicheng Liu @@oth@@ Luming Zhang @@oth@@ Chun Chen @@oth@@ Jiajun Bu @@oth@@ |
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Xiao Liu ddc 004 bkl 54.00 misc low-dimensional subspace misc learning (artificial intelligence) misc Covariance matrices misc semi-supervised splitting misc local maxima misc Estimation misc semi-supervised learning misc random forest misc random forest construction misc hyperplane separation misc kernel-based density estimation misc robust semisupervised node splitting misc labeled training data misc Training data misc unified optimization framework misc Kernel misc data points misc asymptotic mean integrated squared error criterion misc image classification misc Bandwidth misc computer vision misc Training misc supervised algorithms misc high-dimensional feature space misc Radio frequency misc subspace learning misc machine learning tasks misc quality measure misc margin maximization-based semisupervised methods misc node splitting misc optimisation misc Feature extraction misc Face - anatomy & histology misc Image Processing, Computer-Assisted - methods misc Pattern Recognition, Automated - methods Random Forest Construction With Robust Semisupervised Node Splitting |
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004 620 DNB 54.00 bkl Random Forest Construction With Robust Semisupervised Node Splitting low-dimensional subspace learning (artificial intelligence) Covariance matrices semi-supervised splitting local maxima Estimation semi-supervised learning random forest random forest construction hyperplane separation kernel-based density estimation robust semisupervised node splitting labeled training data Training data unified optimization framework Kernel data points asymptotic mean integrated squared error criterion image classification Bandwidth computer vision Training supervised algorithms high-dimensional feature space Radio frequency subspace learning machine learning tasks quality measure margin maximization-based semisupervised methods node splitting optimisation Feature extraction Face - anatomy & histology Image Processing, Computer-Assisted - methods Pattern Recognition, Automated - methods |
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ddc 004 bkl 54.00 misc low-dimensional subspace misc learning (artificial intelligence) misc Covariance matrices misc semi-supervised splitting misc local maxima misc Estimation misc semi-supervised learning misc random forest misc random forest construction misc hyperplane separation misc kernel-based density estimation misc robust semisupervised node splitting misc labeled training data misc Training data misc unified optimization framework misc Kernel misc data points misc asymptotic mean integrated squared error criterion misc image classification misc Bandwidth misc computer vision misc Training misc supervised algorithms misc high-dimensional feature space misc Radio frequency misc subspace learning misc machine learning tasks misc quality measure misc margin maximization-based semisupervised methods misc node splitting misc optimisation misc Feature extraction misc Face - anatomy & histology misc Image Processing, Computer-Assisted - methods misc Pattern Recognition, Automated - methods |
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ddc 004 bkl 54.00 misc low-dimensional subspace misc learning (artificial intelligence) misc Covariance matrices misc semi-supervised splitting misc local maxima misc Estimation misc semi-supervised learning misc random forest misc random forest construction misc hyperplane separation misc kernel-based density estimation misc robust semisupervised node splitting misc labeled training data misc Training data misc unified optimization framework misc Kernel misc data points misc asymptotic mean integrated squared error criterion misc image classification misc Bandwidth misc computer vision misc Training misc supervised algorithms misc high-dimensional feature space misc Radio frequency misc subspace learning misc machine learning tasks misc quality measure misc margin maximization-based semisupervised methods misc node splitting misc optimisation misc Feature extraction misc Face - anatomy & histology misc Image Processing, Computer-Assisted - methods misc Pattern Recognition, Automated - methods |
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Random Forest Construction With Robust Semisupervised Node Splitting |
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Random forest (RF) is a very important classifier with applications in various machine learning tasks, but its promising performance heavily relies on the size of labeled training data. In this paper, we investigate constructing of RFs with a small size of labeled data and find that the performance bottleneck is located in the node splitting procedures; hence, existing solutions fail to properly partition the feature space if there are insufficient training data. To achieve robust node splitting with insufficient data, we present semisupervised splitting to overcome this limitation by splitting nodes with the guidance of both labeled and abundant unlabeled data. In particular, an accurate quality measure of node splitting is obtained by carrying out the kernel-based density estimation, whereby a multiclass version of asymptotic mean integrated squared error criterion is proposed to adaptively select the optimal bandwidth of the kernel. To avoid the curse of dimensionality, we project the data points from the original high-dimensional feature space onto a low-dimensional subspace before estimation. A unified optimization framework is proposed to select a coupled pair of subspace and separating hyperplane such that the smoothness of the subspace and the quality of the splitting are guaranteed simultaneously. Our algorithm efficiently avoids overfitting caused by bad initialization and local maxima when compared with conventional margin maximization-based semisupervised methods. We demonstrate the effectiveness of the proposed algorithm by comparing it with state-of-the-art supervised and semisupervised algorithms for typical computer vision applications, such as object categorization, face recognition, and image segmentation, on publicly available data sets. |
abstractGer |
Random forest (RF) is a very important classifier with applications in various machine learning tasks, but its promising performance heavily relies on the size of labeled training data. In this paper, we investigate constructing of RFs with a small size of labeled data and find that the performance bottleneck is located in the node splitting procedures; hence, existing solutions fail to properly partition the feature space if there are insufficient training data. To achieve robust node splitting with insufficient data, we present semisupervised splitting to overcome this limitation by splitting nodes with the guidance of both labeled and abundant unlabeled data. In particular, an accurate quality measure of node splitting is obtained by carrying out the kernel-based density estimation, whereby a multiclass version of asymptotic mean integrated squared error criterion is proposed to adaptively select the optimal bandwidth of the kernel. To avoid the curse of dimensionality, we project the data points from the original high-dimensional feature space onto a low-dimensional subspace before estimation. A unified optimization framework is proposed to select a coupled pair of subspace and separating hyperplane such that the smoothness of the subspace and the quality of the splitting are guaranteed simultaneously. Our algorithm efficiently avoids overfitting caused by bad initialization and local maxima when compared with conventional margin maximization-based semisupervised methods. We demonstrate the effectiveness of the proposed algorithm by comparing it with state-of-the-art supervised and semisupervised algorithms for typical computer vision applications, such as object categorization, face recognition, and image segmentation, on publicly available data sets. |
abstract_unstemmed |
Random forest (RF) is a very important classifier with applications in various machine learning tasks, but its promising performance heavily relies on the size of labeled training data. In this paper, we investigate constructing of RFs with a small size of labeled data and find that the performance bottleneck is located in the node splitting procedures; hence, existing solutions fail to properly partition the feature space if there are insufficient training data. To achieve robust node splitting with insufficient data, we present semisupervised splitting to overcome this limitation by splitting nodes with the guidance of both labeled and abundant unlabeled data. In particular, an accurate quality measure of node splitting is obtained by carrying out the kernel-based density estimation, whereby a multiclass version of asymptotic mean integrated squared error criterion is proposed to adaptively select the optimal bandwidth of the kernel. To avoid the curse of dimensionality, we project the data points from the original high-dimensional feature space onto a low-dimensional subspace before estimation. A unified optimization framework is proposed to select a coupled pair of subspace and separating hyperplane such that the smoothness of the subspace and the quality of the splitting are guaranteed simultaneously. Our algorithm efficiently avoids overfitting caused by bad initialization and local maxima when compared with conventional margin maximization-based semisupervised methods. We demonstrate the effectiveness of the proposed algorithm by comparing it with state-of-the-art supervised and semisupervised algorithms for typical computer vision applications, such as object categorization, face recognition, and image segmentation, on publicly available data sets. |
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Random Forest Construction With Robust Semisupervised Node Splitting |
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http://dx.doi.org/10.1109/TIP.2014.2378017 http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=6975199 http://www.ncbi.nlm.nih.gov/pubmed/25494503 http://search.proquest.com/docview/1640798292 |
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A unified optimization framework is proposed to select a coupled pair of subspace and separating hyperplane such that the smoothness of the subspace and the quality of the splitting are guaranteed simultaneously. Our algorithm efficiently avoids overfitting caused by bad initialization and local maxima when compared with conventional margin maximization-based semisupervised methods. 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