Relative Karhunen-Loeve transform method for pattern recognition

CLAFIC (class-featuring information compression) is a well-known class feature extraction method. By using Karhunen-Loeve transform (KLT) for patterns in a category, class features for the category are extracted. However, such a class feature may not be suitable for classification, if it is also contained in other categories. Suitable class features for classification have to be contained in a category but not in the other categories. In order to solve this problem, we propose the relative Karhunen-Loeve transform method (RKLTM) for class feature extraction. We show the advantages of RKLTM over CLAFIC by the experiments on handwritten numeral recognition.