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Various internet services, including cloud providers and social networks collect large amounts of information that needs to be processed for statistical or other reasons without breaching user privacy. We present a novel approach where privacy protection can be viewed as a data transformation problem. The problem is formulated as a pair of classification tasks, (a) a privacy-insensitive and (b) a...
This paper aims to tackle the problem of brain network classification with machine learning algorithms using spectra of networks' matrices. Two approaches are discussed: first, linear and tree-based models are trained on the vectors of sorted eigenvalues of the adjacency matrix, the Laplacian matrix and the normalized Laplacian; next, SVM classifier is trained with kernels based on information divergence...
This paper provides an algorithm for simulating improper (or noncircular) complex-valued stationary Gaussian processes. The technique utilizes recently developed methods for multi-variate Gaussian processes from the circulant embedding literature. The method can be performed in O(n log2 n) operations, where n is the length of the desired sequence. The method is exact, except when eigenvalues of prescribed...
This article proposes Soft Dependence Clustering (SDC) algorithm which belongs to the class of spectral clustering methods. On each iteration, SDC performs a hierarchical clustering producing a binary split which greedily maximizes the group dependence score. One of the advantages of SDC is the fact that division of a group into two clusters is done based on the adjustable threshold which has a clear...
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