Joint Structured Graph Learning and Unsupervised Feature Selection

The central task in graph-based unsupervised feature selection (GUFS) depends on two folds, one is to accurately characterize the geometrical structure of the original feature space with a graph and the other is to make the selected features well preserve such intrinsic structure. Currently, most of the existing GUFS methods use a two-stage strategy which constructs graph first and then perform feature selection on this fixed graph. Since the performance of feature selection severely depends on the quality of graph, the selection results will be unsatisfactory if the given graph is of low-quality. To this end, we propose a joint graph learning and unsupervised feature selection (JGUFS) model in which the graph can be adjusted to adapt the feature selection process. The JGUFS objective function is optimized by an efficient iterative algorithm whose convergence and complexity are analyzed in detail. Experimental results on representative benchmark data sets demonstrate the improved performance of JGUFS in comparison with state-of-the-art methods and therefore we conclude that it is promising of allowing the feature selection process to change the data graph.

[1]  Shichao Zhang,et al.  Robust Joint Graph Sparse Coding for Unsupervised Spectral Feature Selection , 2017, IEEE Transactions on Neural Networks and Learning Systems.

[2]  Yueting Zhuang,et al.  Graph Regularized Feature Selection with Data Reconstruction , 2016, IEEE Transactions on Knowledge and Data Engineering.

[3]  Isabelle Guyon,et al.  An Introduction to Variable and Feature Selection , 2003, J. Mach. Learn. Res..

[4]  Feiping Nie,et al.  The Constrained Laplacian Rank Algorithm for Graph-Based Clustering , 2016, AAAI.

[5]  William Zhu,et al.  Sparse Graph Embedding Unsupervised Feature Selection , 2018, IEEE Transactions on Systems, Man, and Cybernetics: Systems.

[6]  Jiawei Han,et al.  Joint Feature Selection and Subspace Learning , 2011, IJCAI.

[7]  Hiroshi Motoda,et al.  Feature Selection for Knowledge Discovery and Data Mining , 1998, The Springer International Series in Engineering and Computer Science.

[8]  Deng Cai,et al.  Unsupervised feature selection for multi-cluster data , 2010, KDD.

[9]  Feiping Nie,et al.  A New Simplex Sparse Learning Model to Measure Data Similarity for Clustering , 2015, IJCAI.

[10]  K. Fan On a Theorem of Weyl Concerning Eigenvalues of Linear Transformations: II. , 1949, Proceedings of the National Academy of Sciences of the United States of America.

[11]  Yong Peng,et al.  Enhanced low-rank representation via sparse manifold adaption for semi-supervised learning , 2015, Neural Networks.

[12]  Shuicheng Yan,et al.  Learning With $\ell ^{1}$-Graph for Image Analysis , 2010, IEEE Transactions on Image Processing.

[13]  Nenghai Yu,et al.  Non-negative low rank and sparse graph for semi-supervised learning , 2012, 2012 IEEE Conference on Computer Vision and Pattern Recognition.

[14]  Qinghua Hu,et al.  Subspace clustering guided unsupervised feature selection , 2017, Pattern Recognit..

[15]  K. Fan On a Theorem of Weyl Concerning Eigenvalues of Linear Transformations I. , 1949, Proceedings of the National Academy of Sciences of the United States of America.

[16]  Jing Liu,et al.  Unsupervised Feature Selection Using Nonnegative Spectral Analysis , 2012, AAAI.

[17]  Jérôme Idier,et al.  Algorithms for Nonnegative Matrix Factorization with the β-Divergence , 2010, Neural Computation.

[18]  Guillermo Sapiro,et al.  Sparse Representation for Computer Vision and Pattern Recognition , 2010, Proceedings of the IEEE.

[19]  Deng Cai,et al.  Laplacian Score for Feature Selection , 2005, NIPS.

[20]  Zi Huang,et al.  Proceedings of the Twenty-Second International Joint Conference on Artificial Intelligence ℓ2,1-Norm Regularized Discriminative Feature Selection for Unsupervised Learning , 2022 .

[21]  Xindong Wu,et al.  Feature Selection by Joint Graph Sparse Coding , 2013, SDM.

[22]  Xuelong Li,et al.  Joint Embedding Learning and Sparse Regression: A Framework for Unsupervised Feature Selection , 2014, IEEE Transactions on Cybernetics.

[23]  Feiping Nie,et al.  Efficient and Robust Feature Selection via Joint ℓ2, 1-Norms Minimization , 2010, NIPS.