Efficient semi-supervised feature selection with noise insensitive trace ratio criterion

Neurocomputing ◽

10.1016/j.neucom.2012.05.031 ◽

2013 ◽

Vol 105 ◽

pp. 12-18 ◽

Cited By ~ 50

Author(s):

Yun Liu ◽

Feiping Nie ◽

Jigang Wu ◽

Lihui Chen

Keyword(s):

Feature Selection ◽

Ratio Criterion ◽

Trace Ratio

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Trace Ratio Criterion Based Large Margin Subspace Learning for Feature Selection

IEEE Access ◽

10.1109/access.2018.2888924 ◽

2019 ◽

Vol 7 ◽

pp. 6461-6472 ◽

Cited By ~ 1

Author(s):

Hui Luo ◽

Jiqing Han

Keyword(s):

Feature Selection ◽

Subspace Learning ◽

Large Margin ◽

Ratio Criterion ◽

Trace Ratio

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Cost-sensitive feature selection in medical data analysis with trace ratio criterion

2014 12th International Conference on Signal Processing (ICSP) ◽

10.1109/icosp.2014.7015169 ◽

2014 ◽

Cited By ~ 1

Author(s):

Chao Li ◽

Cen Shi ◽

Huan Zhang ◽

Chun Hui ◽

Kin-Man Lam ◽

...

Keyword(s):

Feature Selection ◽

Data Analysis ◽

Medical Data ◽

Ratio Criterion ◽

Trace Ratio

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Discriminative Feature Selection via Joint Trace Ratio Criterion and l2,1-norm Regularization

2018 IEEE Symposium on Product Compliance Engineering - Asia (ISPCE-CN) ◽

10.1109/ispce-cn.2018.8805793 ◽

2018 ◽

Author(s):

Zhang Jiang ◽

Mingbo Zhao ◽

Weijian Kong

Keyword(s):

Feature Selection ◽

Discriminative Feature ◽

Ratio Criterion ◽

Trace Ratio

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Consensus Graph Weighting via Trace Ratio Criterion for Multi-view Unsupervised Feature Selection

2019 International Conference on Data Mining Workshops (ICDMW) ◽

10.1109/icdmw.2019.00093 ◽

2019 ◽

Author(s):

Liang Du ◽

Xiaolin Lv

Keyword(s):

Feature Selection ◽

Unsupervised Feature Selection ◽

Ratio Criterion ◽

Trace Ratio

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Trace Ratio Criterion based Discriminative Feature Selection via l2,-norm regularization for supervised learning

Neurocomputing ◽

10.1016/j.neucom.2018.08.040 ◽

2018 ◽

Vol 321 ◽

pp. 1-16 ◽

Cited By ~ 5

Author(s):

Mingbo Zhao ◽

Mingquan Lin ◽

Bernard Chiu ◽

Zhao Zhang ◽

Xue-song Tang

Keyword(s):

Feature Selection ◽

Supervised Learning ◽

Discriminative Feature ◽

Ratio Criterion ◽

L2 Norm ◽

Trace Ratio

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Unsupervised Feature Selection via Unified Trace Ratio Formulation and K-means Clustering (TRACK)

Machine Learning and Knowledge Discovery in Databases - Lecture Notes in Computer Science ◽

10.1007/978-3-662-44845-8_20 ◽

2014 ◽

pp. 306-321 ◽

Cited By ~ 27

Author(s):

De Wang ◽

Feiping Nie ◽

Heng Huang

Keyword(s):

Feature Selection ◽

Unsupervised Feature Selection ◽

Trace Ratio

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Semi-Supervised Dimension Reduction Using Trace Ratio Criterion

IEEE Transactions on Neural Networks and Learning Systems ◽

10.1109/tnnls.2011.2178037 ◽

2012 ◽

Vol 23 (3) ◽

pp. 519-526 ◽

Cited By ~ 67

Author(s):

Yi Huang ◽

Dong Xu ◽

Feiping Nie

Keyword(s):

Dimension Reduction ◽

Ratio Criterion ◽

Supervised Dimension Reduction ◽

Trace Ratio

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ITR-Score algorithm: An efficient Trace ratio criterion based algorithm for supervised dimensionality reduction

The 2011 International Joint Conference on Neural Networks ◽

10.1109/ijcnn.2011.6033213 ◽

2011 ◽

Cited By ~ 4

Author(s):

Mingbo Zhao ◽

Zhao Zhang ◽

Tommy W.S. Chow

Keyword(s):

Dimensionality Reduction ◽

Ratio Criterion ◽

Trace Ratio

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Text style analysis using trace ratio criterion patch alignment embedding

Neurocomputing ◽

10.1016/j.neucom.2014.01.012 ◽

2014 ◽

Vol 136 ◽

pp. 201-212

Author(s):

Peng Tang ◽

Mingbo Zhao ◽

Tommy W.S. Chow

Keyword(s):

Style Analysis ◽

Ratio Criterion ◽

Trace Ratio

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Trace Ratio Criterion for Feature Extraction in Classification

Mathematical Problems in Engineering ◽

10.1155/2014/725204 ◽

2014 ◽

Vol 2014 ◽

pp. 1-9 ◽

Cited By ~ 3

Author(s):

Guoqi Li ◽

Changyun Wen ◽

Wei Wei ◽

Yi Xu ◽

Jie Ding ◽

...

Keyword(s):

Feature Extraction ◽

Discriminant Analysis ◽

Linear Discriminant Analysis ◽

Criterion Function ◽

Linear Discriminant ◽

Ratio Criterion ◽

Trace Ratio ◽

Optimal Feature

A generalized linear discriminant analysis based on trace ratio criterion algorithm (GLDA-TRA) is derived to extract features for classification. With the proposed GLDA-TRA, a set of orthogonal features can be extracted in succession. Each newly extracted feature is the optimal feature that maximizes the trace ratio criterion function in the subspace orthogonal to the space spanned by the previous extracted features.

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