Work

Learning Nonnegative Matrix Factorizations from Compressed Data

Year: 2026

Type: article

Abstract: . We propose a flexible and theoretically supported framework for scalable nonnegative matrix factorization. The goal is to find nonnegative low-rank components directly from compressed measurements, accessing the original data only once or twice. We consider compression through randomized sketching methods that can be adapted to the data or can be oblivious. We formulate optimization problems that only depend on the compressed data but that can recover a nonnegative factorization which closely approximates the original matrix. The defined problems can be approached with a variety of algorithms, and in particular, we discuss variations of the popular multiplicative updates method for these compressed problems. We demonstrate the success of our approaches empirically and validate their performance in real-world applications.

Source: SIAM Journal on Matrix Analysis and Applications

DOI: 10.1137/24m169165x

Authors: Abraar Chaudhry, Elizaveta Rebrova

Institutions: Georgia Institute of Technology, Princeton University

Language: English

Topic: Sparse and Compressive Sensing Techniques

Subfield: Computational Mechanics

Field: Engineering

Domain: Physical Sciences

Open Access status: green

Funder: National Science Foundation

Awards: Outliers are not what they seem: data-aware, flexible, and robust randomized iterative methods, Collaborative Research: Fast, Low-Memory Embeddings for Tensor Data with Applications