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In this paper a Feature Ranking algorithm for classification is proposed, which is based on the notion of Bayes decision border. The method elaborates upon the results of the Decision Border Feature Extraction approach, exploiting properties of eigenvalues and eigenvectors of the orthogonal transformation to calculate the discriminative importance weights of the original features. Non parametric classification...
We present a matrix-free method for the large scale trust region subproblem (TRS), assuming that the approximate Hessian is updated using a minimal-memory BFGS method, where the initial matrix is a scaled identity matrix. We propose a variant of the More-Sorensen method that exploits the eigenstructure of the approximate Hessian, and incorporates both the standard and the hard case. The eigenvalues...
Spectral clustering is a new graph and similarity based clustering algorithm. When the image is too big, it will take a long time to compute affinity matrix and its eigenvalues and eigenvectors. In order to improve the convergent speed of spectral clustering, a two-stage texture segmentation algorithm is proposed in this paper. First, an improved watershed algorithm is used to perform pre-segmentation...
We show that computing (and even approximating) maximum clique and minimum graph coloring for circulant graphs is essentially as hard as in the general case. In contrast, we show that, under additional constraints, e.g., prime order and/or sparseness, graph isomorphism and minimum graph coloring become easier in the circulant case, and we take advantage of spectral techniques for their efficient...
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