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About: This is demo program on global thresholding for image of bright small objects, such as aircrafts in airports. the program include four method, otsu,2DTsallis,PSSIM, Smoothnees Method. Changes:Initial Announcement on mloss.org.

About: Robust sparse representation has shown significant potential in solving challenging problems in computer vision such as biometrics and visual surveillance. Although several robust sparse models have been proposed and promising results have been obtained, they are either for error correction or for error detection, and learning a general framework that systematically unifies these two aspects and explore their relation is still an open problem. In this paper, we develop a halfquadratic (HQ) framework to solve the robust sparse representation problem. By defining different kinds of halfquadratic functions, the proposed HQ framework is applicable to performing both error correction and error detection. More specifically, by using the additive form of HQ, we propose an L1regularized error correction method by iteratively recovering corrupted data from errors incurred by noises and outliers; by using the multiplicative form of HQ, we propose an L1regularized error detection method by learning from uncorrupted data iteratively. We also show that the L1regularization solved by softthresholding function has a dual relationship to Huber Mestimator, which theoretically guarantees the performance of robust sparse representation in terms of Mestimation. Experiments on robust face recognition under severe occlusion and corruption validate our framework and findings. Changes:Initial Announcement on mloss.org.

About: The toolbox is to calculate normalized information measures from a given m by (m+1) confusion matrix for objective evaluations of an abstaining classifier. It includes total 24 normalized information measures based on three groups of definitions, that is, mutual information, information divergence, and cross entropy. Changes:Initial Announcement on mloss.org.

About: DRVQ is a C++ library implementation of dimensionalityrecursive vector quantization, a fast vector quantization method in highdimensional Euclidean spaces under arbitrary data distributions. It is an approximation of kmeans that is practically constant in data size and applies to arbitrarily high dimensions but can only scale to a few thousands of centroids. As a byproduct of training, a tree structure performs either exact or approximate quantization on trained centroids, the latter being not very precise but extremely fast. Changes:Initial Announcement on mloss.org.

About: A MATLAB toolkit for performing generalized regression with equality/inequality constraints on the function value/gradient. Changes:Initial Announcement on mloss.org.

About: Nonnegative Sparse Coding, Discriminative Semisupervised Learning, sparse probability graph Changes:Initial Announcement on mloss.org.

About: Distributed optimization: Support Vector Machines and LASSO regression on distributed data Changes:Initial Upload

About: A library for calculating and accessing generalized Stirling numbers of the second kind, which are used for inference in PoissonDirichlet processes. Changes:Initial Announcement on mloss.org.

About: It's a C++ program for symmetric matrix diagonalization, inversion and principal component anlaysis(PCA). The matrix diagonalization function can also be applied to the computation of singular value decomposition (SVD), Fisher linear discriminant analysis (FLDA) and kernel PCA (KPCA) if forming the symmetric matrix appropriately. Changes:Initial Announcement on mloss.org.

About: Loglinear analysis for highdimensional data Changes:Initial Announcement on mloss.org.
