Subspace semi-supervised fisher discriminant analysis

被引：3

作者：

Hi-tech Innovation Center, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China ^{[1
]}

不详 ^{[2
]}

不详 ^{[3
]}

不详 ^{[4
]}

机构：

[1] Hi-tech Innovation Center, Institute of Automation, Chinese Academy of Sciences

[2] Key Laboratory of Underwater Acoustic Communication and Marine Information Technology, Xiamen University

[3] College of Oceanography and Environmental Science, Xiamen University

[4] School of Electronics Information and Control Engineering, Beijing University of Technology

来源：

Zidonghua Xuebao Acta Auto. Sin. | 2009年 / 12卷 / 1513-1519期

关键词：

Dimensionality reduction; Fisher discriminant analysis (FDA); Manifold regularization; Semi-supervised learning;

D O I：

10.3724/SP.J.1004.2009.01513

中图分类号：

学科分类号：

摘要：

Fisher discriminant analysis (FDA) is a popular method for supervised dimensionality reduction. FDA seeks for an embedding transformation such that the ratio of the between-class scatter to the within-class scatter is maximized. Labeled data, however, often consume much time and are expensive to obtain, as they require the efforts of human annotators. In order to cope with the problem of effectively combining unlabeled data with labeled data to find the embedding transformation, we propose a novel method, called subspace semi-supervised Fisher discriminant analysis (SSFDA), for semi-supervised dimensionality reduction. SSFDA aims to find an embedding transformation that respects the discriminant structure inferred from the labeled data and the intrinsic geometrical structure inferred from both the labeled and unlabeled data. We also show that SSFDA can be extended to nonlinear dimensionality reduction scenarios by applying the kernel trick. The experimental results on face recognition demonstrate the effectiveness of our proposed algorithm. © 2009 Acta Automatica Sinica. All rights reserved.

引用

页码：1513 / 1519

页数：6

共 17 条

[1] Mardia K.V., Kent J.T., Bibby J.M., Multivariate Analysis, (1980)
[2] Duda R.O., Hart P.E., Stork D.G., Pattern Classification (Second Edition), (2000)
[3] Fukunaga K., Introduction to Statistical Pattern Recognition (Second Edition), (1990)
[4] Zhu X.J., Ghahramani Z., Lafferty J., Semi-supervised learning using Gaussian fields and harmonic functions, Proceedings of the 12th International Conference on Machine Learning, pp. 912-919, (2003)
[5] Belkin M., Niyogi P., Sindhwani V., Manifold regularization: a geometric framework for learning from examples, Journal of Machine Learning Research, 7, 11, pp. 2399-2434, (2006)
[6] Sindhwani V., Niyogi P., Belkin M., Beyond the point cloud: from transductive to semi-supervised learning, Proceedings of the 22nd International Conference on Machine Learning, pp. 824-831, (2005)
[7] Zhou D., Bousquet O., Lal T.N., Weston J., Scholkopf B., Learning with local and global consistency, Advances in Neural Information Processing Systems 16, pp. 321-328, (2003)
[8] Cai D., He X.F., Han J.W., Semi-supervised discriminant analysis, Proceedings of the 11th IEEE International Conference on Computer Vision, pp. 1-7, (2007)
[9] Zha Z.J., Mei T., Wang J.D., Wang Z.F., Hua X.S., Graph-based semi-supervised learning with multiple labels, Journal of Visual Communication and Image Representation, 20, 2, pp. 97-103, (2009)
[10] Zhao B., Wang F., Zhang C.S., Song Y.Q., Active model selection for graph-based semi-supervised learning, Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 1881-1884, (2008)

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