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This paper proposes a novel solution for compressive polynomial regression learning. The solution comes in primal and dual closed-forms similar to that of ridge regression. Essentially, the proposed solution stretches the covariance computation by a power term thereby compresses or amplifies the estimation. Our experiments on both synthetic data and real-world data show effectiveness of the proposed...
In this paper we introduce a novel, reduced dimension, Polynomial Regression based Classifier (PRC). The classical PRC expands the observed feature data set by considering higher order data statistics. The herein presented novel PRC preliminary performs projections of the data on suitable subspaces associated with the different classes. The projection operation is followed by discarding the contributions...
Categorization of scenes is a fundamental process of human vision that allows us to efficiently and rapidly analyze our surroundings. Scene classification, the classification of images into semantic categories (e.g., coast, mountains, highways and streets) is a challenging and important problem nowadays. This paper is classifying the scenes using support vector machine with radial basis kernel with...
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