Pramod Kaushik Mudrakarta

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Multiresolution Matrix Factorization (MMF) was recently introduced as a method for finding multiscale structure and defining wavelets on graphs/matrices. In this paper we derive pMMF, a parallel algorithm for computing the MMF factorization. Empirically, the running time of pMMF scales linearly in the dimension for sparse matrices. We argue that this makes(More)
Spectral Clustering as a relaxation of the normalized/ratio cut has become one of the standard graph-based clustering methods. Existing methods for the computation of multiple clusters, corresponding to a balanced k-cut of the graph, are either based on greedy techniques or heuristics which have weak connection to the original motivation of minimizing the(More)
Principal Component Analysis (PCA) is a widely used tool for, e.g., exploratory data analysis, dimensionality reduction and clustering. However, it is well known that PCA is strongly affected by the presence of outliers and, thus, is vulnerable to both gross measurement error and adversarial manipulation of the data. This phenomenon motivates the(More)
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