Please use this identifier to cite or link to this item:
Full metadata record
DC FieldValueLanguage
dc.contributor.authorHou, Junhuien
dc.contributor.authorChau, Lap-Puien
dc.contributor.authorMagnenat-Thalmann, Nadiaen
dc.contributor.authorHe, Yingen
dc.identifier.citationHou, J., Chau, L. P., Magnenat-Thalmann, N., & He, Y. (2017). Sparse Low-Rank Matrix Approximation for Data Compression. IEEE Transactions on Circuits and Systems for Video Technology, 27(5), 1043-1054. doi:10.1109/TCSVT.2015.2513698en
dc.description.abstractLow-rank matrix approximation (LRMA) is a powerful technique for signal processing and pattern analysis. However, its potential for data compression has not yet been fully investigated. In this paper, we propose sparse LRMA (SLRMA), an effective computational tool for data compression. SLRMA extends conventional LRMA by exploring both the intra and inter coherence of data samples simultaneously. With the aid of prescribed orthogonal transforms (e.g., discrete cosine/wavelet transform and graph transform), SLRMA decomposes a matrix into a product of two smaller matrices, where one matrix is made up of extremely sparse and orthogonal column vectors and the other consists of the transform coefficients. Technically, we formulate SLRMA as a constrained optimization problem, i.e., minimizing the approximation error in the least-squares sense regularized by the 0-norm and orthogonality, and solve it using the inexact augmented Lagrangian multiplier method. Through extensive tests on real-world data, such as 2D image sets and 3D dynamic meshes, we observe that: 1) SLRMA empirically converges well; 2) SLRMA can produce approximation error comparable to LRMA but in a much sparse form; and 3) SLRMA-based compression schemes significantly outperform the state of the art in terms of rate–distortion performance.en
dc.format.extent12 p.en
dc.relation.ispartofseriesIEEE Transactions on Circuits and Systems for Video Technologyen
dc.rights© 2017 Institute of Electrical and Electronics Engineers (IEEE). This is the author created version of a work that has been peer reviewed and accepted for publication by IEEE Transactions on Circuits and Systems for Video Technology, Institute of Electrical and Electronics Engineers (IEEE). It incorporates referee’s comments but changes resulting from the publishing process, such as copyediting, structural formatting, may not be reflected in this document. The published version is available at: [].en
dc.subjectDRNTU::Engineering::Computer science and engineeringen
dc.subjectData Compressionen
dc.subjectDRNTU::Engineering::Electrical and electronic engineeringen
dc.titleSparse low-rank matrix approximation for data compressionen
dc.typeJournal Articleen
dc.contributor.schoolSchool of Computer Science and Engineeringen
dc.contributor.schoolSchool of Electrical and Electronic Engineeringen
dc.description.versionAccepted versionen
item.fulltextWith Fulltext-
Appears in Collections:EEE Journal Articles
SCSE Journal Articles
Files in This Item:
File Description SizeFormat 
Sparse Low-Rank Matrix Approximation for Data Compression.pdf4.45 MBAdobe PDFThumbnail

Citations 10

Updated on Jan 18, 2023

Web of ScienceTM
Citations 20

Updated on Jan 24, 2023

Page view(s) 50

Updated on Jan 27, 2023

Download(s) 50

Updated on Jan 27, 2023

Google ScholarTM




Items in DR-NTU are protected by copyright, with all rights reserved, unless otherwise indicated.