M. N. Shanmukha Swamy

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In this paper, we present a method of kernel optimization by maximizing a measure of class separability in the empirical feature space, an Euclidean space in which the training data are embedded in such a way that the geometrical structure of the data in the feature space is preserved. Employing a data-dependent kernel, we derive an effective kernel(More)
This paper presents a new scheme of face image feature extraction, namely, the two-dimensional Fisher linear discriminant. Experiments on the ORL and the UMIST face databases show that the new scheme outperforms the PCA and the conventional PCA + FLD schemes, not only in its computational efficiency, but also in its performance for the task of face(More)
This correspondence is concerned with source localization and classification for scenarios where both the far-field and near-field narrowband sources may coexist. We propose an efficient MUSIC-based solution that requires neither a multidimensional search nor high-order statistics (HOS). We also derive the stochastic Cramér-Rao bound (CRB) for the(More)
In this paper, we examine a new design method for two-dimensional (2-D) recursive digital filters using genetic algorithms (GAs). The design of the 2-D filter is reduced to a constrained minimization problem the solution of which is achieved by the convergence of an appropriate GA. Theoretical results are illustrated by a numerical example. Also, comparison(More)