3,663 research outputs found

    Clustering with feature selection using alternating minimization. Application to computational biology

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    This paper deals with unsupervised clustering with feature selection. The problem is to estimate both labels and a sparse projection matrix of weights. To address this combina-torial non-convex problem maintaining a strict control on the sparsity of the matrix of weights, we propose an alternating minimization of the Frobenius norm criterion. We provide a new efficient algorithm named K-sparse which alternates k-means with projection-gradient minimization. The projection-gradient step is a method of splitting type, with exact projection on the â„“ 1 ball to promote sparsity. The convergence of the gradient-projection step is addressed, and a preliminary analysis of the alternating minimization is made. The Frobenius norm criterion converges as the number of iterates in Algorithm K-sparse goes to infinity. Experiments on Single Cell RNA sequencing datasets show that our method significantly improves the results of PCA k-means, spectral clustering, SIMLR, and Sparcl methods. The complexity of K-sparse is linear in the number of samples (cells), so that the method scales up to large datasets. Finally, we extend K-sparse to supervised classification

    Exploratory Analysis of Functional Data via Clustering and Optimal Segmentation

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    We propose in this paper an exploratory analysis algorithm for functional data. The method partitions a set of functions into KK clusters and represents each cluster by a simple prototype (e.g., piecewise constant). The total number of segments in the prototypes, PP, is chosen by the user and optimally distributed among the clusters via two dynamic programming algorithms. The practical relevance of the method is shown on two real world datasets
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