Abstract:
During the past 20 years, low-rank tensor and matrix decomposition models (LRDMs) have become indispensable tools for signal processing, machine learning, and data scienc...Show MoreMetadata
Abstract:
During the past 20 years, low-rank tensor and matrix decomposition models (LRDMs) have become indispensable tools for signal processing, machine learning, and data science. LRDMs represent high-dimensional, multiaspect, and multimodal data using low-dimensional latent factors in a succinct and parsimonious way. LRDMs can serve a variety of purposes, e.g., data embedding (dimensionality reduction), denoising, latent variable analysis, model parameter estimation, and big data compression; see [1]-[5] for surveys of applications.
Published in: IEEE Signal Processing Magazine ( Volume: 37, Issue: 5, September 2020)