Details

Autor(en) / Beteiligte

• Davenport, Mark A.
• Romberg, Justin

Titel

An Overview of Low-Rank Matrix Recovery From Incomplete Observations

Ist Teil von

• IEEE journal of selected topics in signal processing, 2016-06, Vol.10 (4), p.608-622

Ort / Verlag

New York: IEEE

Erscheinungsjahr

2016

Quelle

IEEE Electronic Library Online

Beschreibungen/Notizen

• Low-rank matrices play a fundamental role in modeling and computational methods for signal processing and machine learning. In many applications where low-rank matrices arise, these matrices cannot be fully sampled or directly observed, and one encounters the problem of recovering the matrix given only incomplete and indirect observations. This paper provides an overview of modern techniques for exploiting low-rank structure to perform matrix recovery in these settings, providing a survey of recent advances in this rapidly-developing field. Specific attention is paid to the algorithms most commonly used in practice, the existing theoretical guarantees for these algorithms, and representative practical applications of these techniques.