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In this paper we describe a method for nonlinear class-specific discriminant learning that is based on Cholesky Decomposition. We show that the optimization problem solved in Class-Specific Kernel Discriminant Analysis is equivalent to that of Low-Rank Kernel Regression using training data independent target vectors. This connection allows us to devise a new Class-Specific Kernel Discriminant Analysis...
There has been a surge of efforts in cross-modal recognition and retrieval in recent multimedia research. Towards this goal, we investigate a multi-modal subspace learning algorithm together with the Dropout regularizer. Inspired by the regularization for neural networks, we propose to aritificially remove the effect of certain amount of feature bins using the probabilistic approach to prevent the...
In this paper a variant of the binary Support Vector Machine classifier that exploits intrinsic and penalty graphs in its optimization problem is proposed. We show that the proposed approach is equivalent to a two-step process where the data is firstly mapped to an optimal discriminant space of the input space and, subsequently, the original SVM classifier is applied. Our approach exploits the underlying...
In this paper we present a novel technique for the selection of global natural scale from discrete wavelet transform. Here we define natural scale as the level associated with most prominent (dominant) eigenvalue. This technique is iterative and does not require full decomposition before finding the optimal wavelet level.
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