A Randomized Generalized Low Rank Approximations Of Matrices Algorithm For High Dimensionality Reduction And Image Compression
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Overview - Duke University
The power method for matrices can be generalized to give the tensor power method. Low-rank matrix factorization: (Keywords: Linear dimensionality reduction, see also list of problems below) (Background: SVD (Singular value decomposition)) A lot of problems in machine learning and statistics can be phrased as low-rank matrix factorization questions.
Sketched Subspace Clustering - arXiv
clustering accuracy, SC methods incur prohibitively high computational complexity when processing large volumes of high-dimensional data. Inspired by random sketching approaches for dimensionality reduction, the present paper introduces a randomized scheme for SC, termed Sketch-SC, tailored for large volumes of high-dimensional data.
IEEE TRANSACTIONS ON SIGNAL PROCESSING 2018 (TO APPEAR) 1
advocate SC with high clustering performance at the price of high computational complexity . The goal of this paper is to introduce a randomized scheme for reducing the computational burden of SC algorithms when the number of data, and possibly their dimensionality, is prohibitively large, while maintaining high levels of clustering accuracy.