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Sufficient Dimensionality Reduction for Visual Sequence Classification

11 years 9 months ago
Sufficient Dimensionality Reduction for Visual Sequence Classification
When classifying high-dimensional sequence data, traditional methods (e.g., HMMs, CRFs) may require large amounts of training data to avoid overfitting. In such cases dimensionality reduction can be employed to find a low-dimensional representation on which classification can be done more efficiently. Existing methods for supervised dimensionality reduction often presume that the data is densely sampled so that a neighborhood graph structure can be formed, or that the data arises from a known distribution. Sufficient dimension reduction techniques aim to find a low dimensional representation such that the remaining degrees of freedom become conditionally independent of the output values. In this paper we develop a novel sequence kernel dimension reduction approach (S-KDR). Our approach does not make strong assumptions on the distribution of the input data. Spatial, temporal and periodic information is combined in a principled manner, and an optimal manifold is learned for the en...
Alex Shyr, Raquel Urtasun, Michael Jordan
Added 08 Apr 2010
Updated 14 May 2010
Type Conference
Year 2010
Where CVPR
Authors Alex Shyr, Raquel Urtasun, Michael Jordan
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