Abstract
Multi-view learning has drawn much attention in the past years to reveal the correlated and complemental information between different views. Feature selection for multi-view data is still a challenge in dimension reduction. Most of the multi-view feature selection methods simply concatenate all views together without capturing the information between different views. In this paper, we propose an embedding framework for multi-view feature selection, Embedding Space based Multi-view Feature Selection (ESMFS). ESMFS comes up with a new concept called mapping consensus to embed all views of data to a unified space. By preserving the manifold information, ESMFS captures the fusing views’ information. ESMFS is suitable for both supervised and unsupervised feature selection. For practical purpose, we propose two methods ES-LRFS and ES-MAFS to illustrate ESMFS framework. Experiments show that ES-LRFS and ES-MAFS are of inclusiveness and efficiency for multi-view feature selection, thus proving the feasibility of ESMFS.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Long, B., Yu, P.S., Zhang, Z.: A general model for multiple view unsupervised learning. In: SIAM International Conference on Data Mining, SDM 2008, Atlanta, Georgia, USA, 24–26 April 2008, pp. 822–833 (2013)
Xia, T., Tao, D., Mei, T., Zhang, Y.: Multiview spectral embedding. IEEE Trans. Syst. Man Cybern. Part B 40(6), 1438–1446 (2010)
Zhang, L., Zhang, Q., Zhang, L., Tao, D., Huang, X., Du, B.: Ensemble manifold regularized sparse low-rank approximation for multiview feature embedding. Pattern Recognit. 48(10), 3102–3112 (2015)
Li, J., Wu, Y., Zhao, J., Lu, K.: Low-rank discriminant embedding for multiview learning. IEEE Trans. Cybern. 47(11), 3516 (2017)
Wan, Y., Chen, X., Zhang, J.: Global and intrinsic geometric structure embedding for unsupervised feature selection. Expert Syst. Appl. (2017)
Wei, X., Cao, B., Yu, P.S.: Multi-view unsupervised feature selection by cross-diffused matrix alignment. In: International Joint Conference on Neural Networks (2017)
Feng, Y., Xiao, J., Zhuang, Y., Liu, X.: Adaptive unsupervised multi-view feature selection for visual concept recognition. In: Lee, K.M., Matsushita, Y., Rehg, J.M., Hu, Z. (eds.) ACCV 2012. LNCS, vol. 7724, pp. 343–357. Springer, Heidelberg (2013). https://doi.org/10.1007/978-3-642-37331-2_26
Tang, J., Hu, X., Gao, H., Liu, H.: Unsupervised feature selection for multi-view data in social media (2013)
Qian, M., Zhai, C.: Unsupervised feature selection for multi-view clustering on text-image web news data. In: ACM International Conference on Conference on Information and Knowledge Management, pp. 1963–1966 (2014)
Zhang, T., Tao, D., Li, X., Yang, J.: Patch alignment for dimensionality reduction. IEEE Trans. Knowl. Data Eng. 21(9), 1299–1313 (2009)
Du, L., Shen, Y.D.: Unsupervised feature selection with adaptive structure learning, vol. 37, no. 7, pp. 209–218 (2015)
Li, Z., Yang, Y., Liu, J., Zhou, X., Lu, H.: Unsupervised feature selection using nonnegative spectral analysis. In:
, vol. 2, pp. 1026–1032 (2012)Wei, X., Cao, B., Yu, P.S.: Nonlinear joint unsupervised feature selection. In: SIAM International Conference on Data Mining, pp. 414–422 (2016)
He, X., Cai, D., Niyogi, P.: Laplacian score for feature selection. In: International Conference on Neural Information Processing Systems, pp. 507–514 (2006)
Kumar, A., Rai, P.: Co-regularized multi-view spectral clustering. In: International Conference on Neural Information Processing Systems, pp. 1413–1421 (2011)
Qian, M., Zhai, C.: Robust unsupervised feature selection. In: International Joint Conference on Artificial Intelligence, pp. 1621–1627 (2013)
Chen, X., Zhou, G., Chen, Y., Shao, G., Gu, Y.: Supervised multiview feature selection exploring homogeneity and heterogeneity with \(l_{1,2}\)-norm and automatic view generation. IEEE Trans. Geosci. Remote Sens. PP(99), 1–15 (2017)
Chen, X., Liu, W., Su, F., Zhou, G.: Semisupervised multiview feature selection for VHR remote sensing images with label learning and automatic view generation. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. PP(99), 1–13 (2017)
Chen, X., Liu, W., Su, F., Shao, G.: Semi-supervised multiview feature selection with label learning for VHR remote sensing images. In: Geoscience and Remote Sensing Symposium, pp. 2372–2375 (2016)
Chen, X., Song, L., Hou, Y., Shao, G.: Efficient semi-supervised feature selection for VHR remote sensing images. In: Geoscience and Remote Sensing Symposium, pp. 1500–1503 (2016)
Roweis, S.T., Saul, L.K.: Nonlinear dimensionality reduction by locally linear embedding. Science 290(5500), 2323 (2000)
Tenenbaum, J.B., Silva, V.D., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000)
Belkin, M., Niyogi, P.: Laplacian eigenmaps and spectral techniques for embedding and clustering. In: Advances in Neural Information Processing Systems, vol. 14, no. 6 (2001)
Donoho, D.L., Grimes, C.: Hessian eigenmaps: locally linear embedding techniques for high-dimensional data. Proc. Natl. Acad. Sci. U.S.A. 100(10), 5591 (2003)
Zhang, Z.Y., Zha, H.Y.: Principal manifolds and nonlinear dimensionality reduction via tangent space alignment. Adv. Manuf.
) 8(4), 406–424 (2004)Wang, T., Zhao, D., Tian, S.: An overview of kernel alignment and its applications. Artif. Intell. Rev. 43(2), 179–192 (2015)
, vol. 2, pp. 1026–1032 (2012)
) 8(4), 406–424 (2004)Acknowledgment
This research is supported by the National Natural Science Foundation of China (No. 61573012).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Zhang, J., Wan, Y., Pan, Y. (2018). A Framework for Multi-view Feature Selection via Embedding Space. In: Wang, Y., Jiang, Z., Peng, Y. (eds) Image and Graphics Technologies and Applications. IGTA 2018. Communications in Computer and Information Science, vol 875. Springer, Singapore. https://doi.org/10.1007/978-981-13-1702-6_6
Download citation
DOI: https://doi.org/10.1007/978-981-13-1702-6_6
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-13-1701-9
Online ISBN: 978-981-13-1702-6
eBook Packages: Computer ScienceComputer Science (R0)