Local graph cut criterion for supervised dimensionality reduction

Xiangrong Zhang; Sisi Zhou; Licheng Jiao

doi:10.1117/12.832411

30 October 2009 Local graph cut criterion for supervised dimensionality reduction

Xiangrong Zhang, Sisi Zhou, Licheng Jiao

Proceedings Volume 7496, MIPPR 2009: Pattern Recognition and Computer Vision; 74962I (2009) https://doi.org/10.1117/12.832411
Event: Sixth International Symposium on Multispectral Image Processing and Pattern Recognition, 2009, Yichang, China

Abstract

Graph cut criterion has been proven to be robust and applicable in clustering problems. In this paper the graph cut criterion is applied to construct a supervised dimensionality reduction. A new graph cut, scaling cut, is proposed based on the classical normalized cut. Scaling cut depicts the relationship between samples, which makes it can handle the heteroscedastic and multimodel data in which LDA fails. Meanwhile, the solution to scaling cut is global optimal for it is a generalized eigenvalue problem. To obtain a more reasonable projection matrix and reduce the computational complexity as well, the localized k-nearest neighbor graph is introduced in, which leads to equivalent or better results compared with scaling cut.

Citation Download Citation

Xiangrong Zhang, Sisi Zhou, and Licheng Jiao "Local graph cut criterion for supervised dimensionality reduction", Proc. SPIE 7496, MIPPR 2009: Pattern Recognition and Computer Vision, 74962I (30 October 2009); https://doi.org/10.1117/12.832411

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