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In this paper, we propose a novel method of label propagation for one-class learning. For binary (positive/negative) classification, the proposed method simultaneously measures the pair-wise similarity between samples and the negativity at every sample based on a cone-based model of local neighborhoods. Relying only on positive labeled samples as in one-class learning, the method estimates the labels...
We propose cone-restricted kernel subspace methods for pattern classification. A cone is mathematically defined in a manner similar to a linear subspace with a nonnegativity constraint. Since the angles between vectors (i.e., inner products) are fundamental to the cone, kernel tricks can be directly applied. The proposed methods approximate the distribution of sample patterns by using the cone in...
Kernel-based methods, e.g., support vector machine (SVM), produce high classification performances. However, the computation becomes time-consuming as the number of the vectors supporting the classifier increases. In this paper, we propose a method for reducing the computational cost of classification by kernel-based methods while retaining the high performance. By using linear algebra of a kernel...
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