S3CCA: Smoothly Structured Sparse CCA for Partial Pattern Matching
Takumi Kobayashi · 2014
The partial pattern matching is fundamental for pattern recognition to compare the pair of input patterns by exploiting the common features shared by those patterns while excluding the irrelevant ones. In this paper, for the pattern matching, we propose a novel method of smoothly structured sparse canonical correlation analysis, called S3CCA. The proposed method works on the feature matrix composed of a (local) feature dimension and an array dimension. In the framework of CCA, the method provides map weights along the array dimension to depict the parts that exhibit the common/similar features across the pair of feature matrices. By introducing the appropriate regularization into CCA, the map weights are optimized so as to be both smooth and localized, i.e., structured sparse. Thereby, the common features are effectively detected by the smooth and well-localized weights to improve the matching performance. In the experiments on pattern matching as well as classification based on the matching, the proposed method produces the favorable performance compared to the other methods.