NDLI: Rank minimization for subspace tracking from incomplete data

Content Provider	IEEE Xplore Digital Library
Author	Mardani, M. Mateos, G. Giannakis, G.B.
Copyright Year	2013
Description	Author affiliation: Dept. of ECE, Univ. of Minnesota, Minneapolis, MN, USA (Mardani, M.; Mateos, G.; Giannakis, G.B.)
Abstract	Extracting latent low-dimensional structure from high-dimensional data is of paramount importance in timely inference tasks encountered with `Big Data' analytics. However, increasingly noisy, heterogeneous, and incomplete datasets as well as the need for real-time processing pose major challenges towards achieving this goal. In this context, the fresh look advocated here permeates benefits from rank minimization to track low-dimensional subspaces from incomplete data. Leveraging the low-dimensionality of the subspace sought, a novel estimator is proposed based on an exponentially-weighted least-squares criterion regularized with the nuclear norm. After recasting the non-separable nuclear norm into a form amenable to online optimization, a real-time algorithm is developed and its convergence established under simplifying technical assumptions. The novel subspace tracker can asymptotically offer the well-documented performance guarantees of the batch nuclear-norm regularized estimator. Simulated tests with real Internet data confirm the efficacy of the proposed algorithm in tracking the traffic subspace, and its superior performance relative to state-of-the-art alternatives.
Starting Page	5681
Ending Page	5685
File Size	542556
Page Count	5
File Format	PDF
ISBN	9781479903566
ISSN	15206149
DOI	10.1109/ICASSP.2013.6638752
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2013-05-26
Publisher Place	Canada
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Abstracts Indexes matrix completion Low rank online algorithm
Content Type	Text
Resource Type	Article

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

K-subspaces with missing data

Giannakis, “Rank minimization for subspace tracking from incomplete data (2013)

A robust online subspace estimation and tracking algorithm

High-rank matrix completion and subspace clustering with missing data (2011).

Online Bayesian low-rank subspace learning from partial observations

High-rank matrix completion.

Low-rank matrix completion using alternating minimization

Online Low-Rank Subspace Learning from Incomplete Data: A Bayesian View

1 set: an algorithm for consistent matrix completion (909).

Rank minimization for subspace tracking from incomplete data

Similar Documents

K-subspaces with missing data

Giannakis, “Rank minimization for subspace tracking from incomplete data (2013)

A robust online subspace estimation and tracking algorithm

High-rank matrix completion and subspace clustering with missing data (2011).

Online Bayesian low-rank subspace learning from partial observations

High-rank matrix completion.

Low-rank matrix completion using alternating minimization

Online Low-Rank Subspace Learning from Incomplete Data: A Bayesian View

1 set: an algorithm for consistent matrix completion (909).

Rank minimization for subspace tracking from incomplete data