Probabilistic tangent subspace: a unified view

Authors:
Jianguo Lee;Jingdong Wang;Changshui Zhang;Zhaoqi Bian
Affiliations:
Tsinghua University, Beijing, P. R. China;Tsinghua University, Beijing, P. R. China;Tsinghua University, Beijing, P. R. China;Tsinghua University, Beijing, P. R. China
Venue:
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Year:
2004

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Abstract

Tangent Distance (TD) is one classical method for invariant pattern classification. However, conventional TD need pre-obtain tangent vectors, which is difficult except for image objects. This paper extends TD to more general pattern classification tasks. The basic assumption is that tangent vectors can be approximately represented by the pattern variations. We propose three probabilistic subspace models to encode the variations: the linear subspace, nonlinear subspace, and manifold subspace models. These three models are addressed in a unified view, namely Probabilistic Tangent Subspace (PTS). Experiments show that PTS can achieve promising classification performance in non-image data sets.