Oriented soft localized subspace classification

Subspace methods of pattern recognition form an interesting and popular classification paradigm. The earliest subspace method of classification was the class featuring information compression (CLAFIC) which associated with each class a linear subspace. Local subspace classification methodologies which have enhanced classification power by associating multiple linear subspaces with each class have also been investigated. In this paper, we introduce the oriented soft regional subspace classifier (OS-RSC). The highlights of this classifier are: (i) class specific subspaces are formed to specifically maximize the average projection of one class while minimizing that of the rival class; (ii) multiple manifolds are formed for each class increasing the classification power; and (iii) soft sharing of the training patterns again allows for consistent classification performance. It turns out that the cost function for forming class specific subspaces is maximized for a subspace of unit dimensionality. The performance of the proposed classifier is tested on real-world classification problems