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Least desirable feature elimination in a general pattern recognition problem
Affiliation:1. Research Institute for the Development of Digital Technologies and Artificial Intelligence, 17A, Boz-2, Tashkent, 100125, Uzbekistan;2. Institute of Fundamental and Applied Research, TIIAME National Research University, 39, Qori Niyaziy str., Tashkent, 100000, Uzbekistan;3. Tashkent University of Information Technologies named after Muhammad al-Khwarizmi, 108, Amir Temur str., Tashkent, 100200, Uzbekistan;1. Research Institute for the Development of Digital Technologies and Artificial Intelligence, 17A, Boz-2, Tashkent, 100125, Uzbekistan;2. Institute of Fundamental and Applied Research, TIIAME National Research University, 39, Qori Niyaziy str., Tashkent, 100000, Uzbekistan;1. Research Institute for the Development of Digital Technologies and Artificial Intelligence, 17A, Boz-2, Tashkent, 100125, Uzbekistan;2. Institute of Fundamental and Applied Research, THAME National Research University, 39, Qori Niyaziy str, Tashkent, 100000, Uzbekistan;3. Tashkent University of Information Technologies named after Muhammad al-Khwarizmi, 108, Amir Temur str, Tashkent, 100200, Uzbekistan;4. Samarkand State University, 15, University boulevard, Samarkand, 140104, Uzbekistan
Abstract:A new technique for dimensionality reduction for a general pattern recognition problem with pattern classes represented by multivariate normal distributions is presented. The method consists of identifying and eliminating the least desirable feature out of the original feature space. It is shown that great simplicity is obtained by doing so in comparison to the existing methods. A very simple expression describing a vector representing the least desirable feature in a most general case is derived. If the original feature space is N-dimensional then recognizing and eliminating such a feature is equivalent to selecting and retaining the N−1 best features. J-divergence is used as a measure of the discrimination between the classes. The flow chart for the method for discarding more than one dimension is presented.
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