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Subspace selection is widely adopted in many areas of pattern recognition. A recent result, named maximizing the geometric mean of Kullback-Leibler (KL) divergences of class pairs (MGMD), is a successful method for subspace selection, which can significantly reduce the class separation problem. However, in many applications, labeled data are very limited while unlabeled data can be easily obtained...
Fisher's linear discriminant analysis (FLDA) is one of the most well-known linear subspace selection methods. However, FLDA suffers from the class separation problem. The projection to a subspace tends to merge close class pairs. Recent results show that maximizing the geometric mean or harmonic mean of Kullback-Leibler (KL) divergences of class pairs can significantly reduce this problem. In this...
Color-to-gray is a classical manipulation in image processing. In this paper, a kernel based method is presented to carry out color-to-gray operation. It is noticeable that, in a meaningful color image only the linear combination of the three color components of every pixel is hardly enough to describe the implication relation between the luminance and chrominance in every pixel, or between pixels...
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