Triple Component Matrix Factorization: Untangling Global, Local, and Noisy Components

arXiv:2404.07955v1 Announce Type: new
Abstract: In this work, we study the problem of common and unique feature extraction from noisy data. When we have N observation matrices from N different and associated sources corrupted by sparse and potentially gross noise, can we recover the common and unique components from these noisy observations? This is a challenging task as the number of parameters to estimate is approximately thrice the number of observations. Despite the difficulty, we propose an intuitive alternating minimization …

abstract arxiv components cs.lg data extraction factorization feature feature extraction global math.st matrix noise observation stat.th study type work