---
title: "Matrix Co-Factorization on Compressed Sensing"
authors:
  - "Jiho Yoo"
  - "Seungjin Choi"
doi: "10.5591/978-1-57735-516-8/IJCAI11-268"
url: "https://ijcai.org/proceedings/2011/268"
pdf: "https://ijcai.org/proceedings/2011/0268.pdf"
venue: "Proceedings of the Twenty-Second International Joint Conference on Artificial Intelligence"
year: 2011
pages: "1595-1601"
track: ""
keywords:
---

# Matrix Co-Factorization on Compressed Sensing

Jiho Yoo, Seungjin Choi

*Proceedings of the Twenty-Second International Joint Conference on Artificial Intelligence*, pages 1595-1601. DOI: [10.5591/978-1-57735-516-8/IJCAI11-268](https://doi.org/10.5591/978-1-57735-516-8/IJCAI11-268)

## Abstract

In this paper we address the problem of matrix factorization on compressively-sampled measurements which are obtained by random projections. While this approach improves the scalability of matrix factorization, its performance is not satisfactory. We present a matrix co-factorization method where compressed measurements and a small number of uncompressed measurements are jointly decomposed, sharing a factor matrix. We evaluate the performance of three matrix factorization methods in terms of Cram{'e}r-Rao bounds, including: (1) matrix factorization on uncompressed data (MF); (2) matrix factorization on compressed data (CS-MF); (3) matrix co-factorization on compressed and uncompressed data (CS-MCF). Numerical experiments demonstrate that CS-MCF improves the performance of CS-MF, emphasizing the useful behavior of exploiting side information (a small number of uncompressed measurements).