Abstract
The recent literature indicates that structure preserving is of great importance for feature selection and many existing selection criteria essentially work in this way. In this paper, we argue that the Eigen value decomposition of global pair wise similarity matrix should be weighted, and the redundancy among the features should be minimized. In order to show this, we propose a weighted structure preservation and features redundancy minimization framework for feature selection. In this framework, the Eigen vector obtained by the Eigen decomposition of global pair wise similarity matrix is weighted by the corresponding Eigen value, and the cosine distance between two features together with the L2,1 norm of these two features are used to evaluate the degree of redundancy between these two features. A comprehensive experimental study is then conducted in order to compare our feature selection algorithms with many state-of-the art ones in supervised learning scenarios. The conducted experiments validate the effectiveness of our feature selection.
Similar content being viewed by others
References
Benabdeslem K, Hindawi M (2014) Efficient semi-supervised feature selection: constraint, relevance and redundancy. IEEE Trans Knowl Data Eng 26(5):1131–1143
Burkhardt F, Paeschke A, Rolfes M, Sendlmeier WF, Weiss B (2005) A database of German emotional speech. In: Proceedings of INTERSPEECH, Lisbon, pp 1517–1520
Cai D, Zhang C, He X (2010) Unsupervised feature selection for multi-cluster data. In: Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining, pp 333–342
Chen M, Tsang IW, Tan M, Cham TJ (2015) A unified feature selection framework for graph embedding on high dimensional data. IEEE Trans Knowl Data Eng 27(6):1465–477
Chen J, Jiao L, Wen Z (2016) High-level feature selection with dictionary learning for unsupervised SAR imagery terrain classification. IEEE J Sel Top Appl Earth Obs Remote Sens pp(99):1–16
Du X, Yan Y, Pan P, Long G, Zhao L (2016) Multiple graph unsupervised feature selection. Signal Processing 120:754–760
Eyben F, Wöllmer M, Schuller B (2010) OpenSMILE —the munich versatile and fast open-source audio feature extractor. In: Proceedings of the ACM multimedia (MM), Florence, Italy, pp 1459–1462
Fang X, Xu Y, Li X, Fan Z, Liu H, Chen Y (2014) Locality and similarity preserving embedding for feature selection. IEEE Trans Cybern 128:304–315
Han J, Sun Z, Hao H (2016) \(l_{0}\)-norm based structural sparse least square regression for feature selection. Pattern Recognition 48(12):3927–3940
Haq S, Jackson PJB (2009) Speaker-dependent audio–visual emotion recognition. In: Proceedings of AVSP, pp 53–58
He X, Niyogi P (2003) Locality preserving projections. In: Proceedings of the advances in neural information processing systems (NIPS), Vancouver, pp 585–591
Hou C, Nie F, Li X, Yi D, Wu Y (2014) Joint embedding learning and sparse regression: a framework for unsupervised feature selection. IEEE Trans Cybern 44(6):793–804
Lee K-C, Ho J, Kriegman D (2005) Acquiring linear subspaces for face recognition under variable lighting. IEEE Trans Pattern Anal Mach Intell 27(5):684–698
Li Z, Tang J (2015) Unsupervised feature selection via nonnegative spectral analysis and redundancy control. IEEE Trans Image Process 24(12):5343–5355
Liang Y, Liao S, Wang L, Zou B (2011) Exploring regularized feature selection for person specific face verification. In: Proceedings of the international conference on computer vision, pp 1676–1683
Liao Y, Vemuri VR (2002) Use of K-nearest neighbor classifier for intrusion detection. Comput Secur 21(5):439–448
Liu X, Wang L, Zhang J, Yin J, Liu H (2015) Global and local structure preservation for feature selection. IEEE Trans Cybern 25(6):1083–1095
Martinez AM, Kak AC (2001) PCA versus LDA. IEEE Trans Pattern Anal Mach Intell 23(2):228–233
Mohsenzadeh Y, Sheikhzadeh H, Reza AM, Bathaee N, Kalayeh MM (2013) The relevance sample-feature machine: a sparse Bayesian learning approach to joint feature-sample selection. IEEE Trans Cybern 43(6):2241–2254
Nene SA, Nayar SK, Murase H (Feb 1996) Columbia object image library (COIL-20), Technical Report CUCS-005-96
Nie F, Huang H, Cai X, Ding C (2010) Efficient and robust feature selection via joint \(L_{2,1}\)-norms minimization. In: Proceedings of the neural information processing systems
Nie F, Huang H, Cai X, Ding C (2011) Structured sparse model based feature selection and classification for hyperspectral imagery. In: Proceedings of the IEEE international geoscience and remote sensing symposium, pp 1771–1774
ORLface database. http://www.cl.cam.ac.uk/research/dtg/attarchive/facedatabase.html
Peng H, Long F, Ding C (2005) Feature selection based on mutual information: criteria of max-dependency, max-relevance, and min-redundancy. IEEE Trans Pattern Anal Mach Intell 27(8):1226–1238
Roweis S, Saul L (2000) Nonlinear dimensionality reduction by locally linear embedding. Science 290(5500):2323–2326
Shi C, Ruan Q, An G (2014) Sparse feature selection based on graph Laplacian for web image annotation. Image Vis Comput 32(3):189–201
Shi C, Ruan Q, An G, Ge C (2015) Semi-supervised sparse feature selection based on multi-view Laplacian regularization. Image Vis Comput 41:1–10
Sim T, Barker S, Bsat M (2013) The CMU pose, illumination, and expression database. IEEE Trans Pattern Anal Mach Intell 25(12):1615–1618
Sun Y, Wen G (2015) Adaptive feature transformation for classification with sparse representation. Int J Light Electron Opt 126(23):4452–4459
Sun Y, Todorovic S, Goodison S (2010) Local-learning-based feature selection for high-dimensional data analysis. IEEE Trans Pattern Anal Mach Intell 32(9):1610–1626
The selected speech emotion database of Institute of Automation Chinese Academy of Sciences (CASIA). http://www.datatang.com/data/39277
The web page of Cai. http://www.cad.zju.edu.cn/home/dengcai/Data/
Tsimpiris A, Vlachos I, Kugiumtzis D (2014) Nearest neighbor estimate of conditional mutual information in feature selection. Expert Syst Appl 39:12697–12708
UCI datasets. http://archive.ics.uci.edu/ml/
USPS database. http://www-i6.informatik.rwth-aachen.de/~keysers/usps.html
Wang C, Cao L, Miao B (2013) Optimal feature selection for sparse linear discriminant analysis and its applications in gene expression data. Comput Stat Data Anal 66:140–149
Wang JJ-Y, Bensmail H, Gao X (2014) Feature selection and multi-kernel learning for sparse representation on a manifold. Neural Netw 51:9–16
Wang D, Nie F, Huang H (2015) Feature selection via global redundancy minimization. IEEE Trans Knowl Data Eng 27(10):2743–2755
Wanga J, Wua L, Kong J, Li Y, Zhang B (2013) Maximum weight and minimum redundancy: a novel framework for feature subset selection. Pattern Recognit 181:1616–1623
Wu Y, Wang C, Bu J, Chen C (2016) Group sparse feature selection on local learning based clustering. Neurocomputing 171:1118–1130
Xiang S, Nie F, Meng G, Pan C, Zhang C (2012) Discriminative least squares regression for multiclass classification and feature selection. IEEE Trans Neural Netw Learn Syst 23(11):1738–1754
Xie Z, Xu Y (2014) Sparse group LASSO based uncertain feature selection. Int J Mach Learn Cybern 5(2):201–210
Yale face database. http://vision.ucsd.edu/content/yale-face-database
Zhang Q, Tian Y, Yang Y, Pan C (2015) Automatic spatial–spectral feature selection for hyperspectral image via discriminative sparse multimodal learning. IEEE Trans Geosci Remote Sens 53(1):261–279
Zhang J, Yu J, Wan J, Zeng Z (2015) l2,1 norm regularized fisher criterion for optimal feature selection. Neurocomputing 166:455–463
Zhang Q, Tian Y, Yang Y, Pan C (2015) Automatic spatial–spectral feature selection for hyperspectral image via discriminative sparse multimodal learning. IEEE Trans Cybern 128:261–279
Zhao Z, Wang L, Liu H, Ye J (2012) Web image annotation via subspace-sparsity collaborated feature selection. IEEE Trans Multimed 14(4):1021–1031
Zhao Z, Wang L, Liu H, Ye J (2013) On similarity preserving feature selection. IEEE Trans Knowl Data Eng 25(3):619–632
Zhao Z, He X, Cai D, Zhang L, Ng W, Zhuang Y (2016) Graph regularized feature selection with data reconstruction. IEEE Trans Knowl Data Eng 28(3):689–700
Zhou N, Yangyang X, Cheng H, Fang J, Pedrycz W (2016) Global and local structure preserving sparse subspace learning: an iterative approach to unsupervised feature selection. Pattern Recognit 53:87–101
Zhu Y, Zhong Z, Cao W, Cheng D (2016) Graph feature selection for dementia diagnosis. Neurocomputing 195:19–22
Acknowledgements
This work was supported by China National Science Foundation under Grants 61273363, 61003174, State Key Laboratory of Brain and Cognitive Science under Grants 08B12. Jiaxing National Science Foundation under Grants 2016AY13013.
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Conflict of interest
The authors declare that they have no conflict of interest.
Additional information
Communicated by V. Loia.
Rights and permissions
About this article
Cite this article
Ye, Q., Sun, Y. Weighted structure preservation and redundancy minimization for feature selection. Soft Comput 22, 7255–7268 (2018). https://doi.org/10.1007/s00500-017-2727-z
Published:
Issue Date:
DOI: https://doi.org/10.1007/s00500-017-2727-z