A Review on Dimensionality Reduction Techniques
暂无分享,去创建一个
Lei Wu | Xuan Huang | Yinsong Ye | Xuan Huang | Y. Ye | Lei Wu | Yinsong Ye
[1] Petros Drineas,et al. Feature selection for linear SVM with provable guarantees , 2014, Pattern Recognit..
[2] Duoqian Miao,et al. A rough set approach to feature selection based on ant colony optimization , 2010, Pattern Recognit. Lett..
[3] Jürgen Schmidhuber,et al. Deep learning in neural networks: An overview , 2014, Neural Networks.
[4] Peter C. Y. Chen,et al. Hierarchical discriminant manifold learning for dimensionality reduction and image classification , 2015, J. Electronic Imaging.
[5] Jason Weston,et al. Gene Selection for Cancer Classification using Support Vector Machines , 2002, Machine Learning.
[6] Geoffrey E. Hinton,et al. Deep Learning , 2015, Nature.
[7] H. Sebastian Seung,et al. Learning the parts of objects by non-negative matrix factorization , 1999, Nature.
[8] K. Thangavel,et al. Dimensionality reduction based on rough set theory: A review , 2009, Appl. Soft Comput..
[9] Qiang Yang,et al. A Survey on Transfer Learning , 2010, IEEE Transactions on Knowledge and Data Engineering.
[10] H. Zou,et al. Regularization and variable selection via the elastic net , 2005 .
[11] Dao-Qiang Zhang,et al. Experimental Comparisons of Semi-Supervised Dimensional Reduction Methods: Experimental Comparisons of Semi-Supervised Dimensional Reduction Methods , 2011 .
[12] Patrik O. Hoyer,et al. Non-negative Matrix Factorization with Sparseness Constraints , 2004, J. Mach. Learn. Res..
[13] Geoffrey E. Hinton,et al. Reducing the Dimensionality of Data with Neural Networks , 2006, Science.
[14] Peter Bühlmann. Regression shrinkage and selection via the Lasso: a retrospective (Robert Tibshirani): Comments on the presentation , 2011 .
[15] R. Tibshirani,et al. Regression shrinkage and selection via the lasso: a retrospective , 2011 .
[16] Rossitza Setchi,et al. Feature selection using Joint Mutual Information Maximisation , 2015, Expert Syst. Appl..
[17] Hujun Yin,et al. Nonlinear dimensionality reduction and data visualization: A review , 2007, Int. J. Autom. Comput..
[18] Smriti Srivastava,et al. Feature Extraction Methods for Speaker Recognition: A Review , 2017, Int. J. Pattern Recognit. Artif. Intell..
[19] J. Tenenbaum,et al. A global geometric framework for nonlinear dimensionality reduction. , 2000, Science.
[20] Qinghua Hu,et al. Feature selection with test cost constraint , 2012, ArXiv.
[21] Teuvo Kohonen,et al. The self-organizing map , 1990 .
[22] Jiehua Zhu,et al. Manifold learning: Dimensionality reduction and high dimensional data reconstruction via dictionary learning , 2016, Neurocomputing.
[23] S T Roweis,et al. Nonlinear dimensionality reduction by locally linear embedding. , 2000, Science.
[24] Duncan Fyfe Gillies,et al. A Review of Feature Selection and Feature Extraction Methods Applied on Microarray Data , 2015, Adv. Bioinformatics.
[25] Dae-Won Kim,et al. Mutual Information-based multi-label feature selection using interaction information , 2015, Expert Syst. Appl..
[26] Xin Yao,et al. A Survey on Evolutionary Computation Approaches to Feature Selection , 2016, IEEE Transactions on Evolutionary Computation.
[27] Barbara Hammer,et al. Data visualization by nonlinear dimensionality reduction , 2015, WIREs Data Mining Knowl. Discov..
[28] Michael E. Tipping,et al. Probabilistic Principal Component Analysis , 1999 .
[29] Bor-Chen Kuo,et al. A Kernel-Based Feature Selection Method for SVM With RBF Kernel for Hyperspectral Image Classification , 2014, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.
[30] David Casasent,et al. An improvement on floating search algorithms for feature subset selection , 2009, Pattern Recognit..
[31] Lei Yu,et al. Sparse multiple maximum scatter difference for dimensionality reduction , 2017, Digit. Signal Process..
[32] Ivor W. Tsang,et al. A Feature Selection Method for Multivariate Performance Measures , 2011, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[33] Pablo A. Estévez,et al. A review of feature selection methods based on mutual information , 2013, Neural Computing and Applications.
[34] Gabriele Steidl,et al. Combined SVM-Based Feature Selection and Classification , 2005, Machine Learning.
[35] Josef Kittler,et al. Floating search methods in feature selection , 1994, Pattern Recognit. Lett..
[36] D. Donoho,et al. Hessian eigenmaps: Locally linear embedding techniques for high-dimensional data , 2003, Proceedings of the National Academy of Sciences of the United States of America.
[37] Kazuyuki Murase,et al. A new wrapper feature selection approach using neural network , 2010, Neurocomputing.
[38] Robert Jenssen,et al. Kernel Entropy Component Analysis , 2010, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[39] Beatriz de la Iglesia,et al. Evolutionary computation for feature selection in classification problems , 2013, WIREs Data Mining Knowl. Discov..
[40] Christopher M. Bishop,et al. GTM: The Generative Topographic Mapping , 1998, Neural Computation.
[41] Yiu-Ming Cheung,et al. Discriminant Manifold Learning via Sparse Coding for Robust Feature Extraction , 2017, IEEE Access.
[42] Dae-Won Kim,et al. Feature selection for multi-label classification using multivariate mutual information , 2013, Pattern Recognit. Lett..
[43] Yao Zhao,et al. A dimensionality reduction method based on structured sparse representation for face recognition , 2016, Artificial Intelligence Review.
[44] Zehang Sun,et al. Object detection using feature subset selection , 2004, Pattern Recognit..