Methods for factorization and approximation of tensors by partial fiber sampling

Cesar F. Caiafa, Andrzej Cichocki

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

In this paper we present, discuss and compare new methods for the reconstruction of tensors by partial sampling, i.e. based on the information contained only in a subset of their entries. As a generalization of the CUR matrix decomposition, which approximates a matrix from a subset of its rows and columns, we present two methods called Tree-CUR and FSTD (Fiber Sampling Tensor Decomposition) for estimating a tensor by using a subset of its n-mode fibres, i.e. some row, column and tube fibers for a 3-dimensional tensor case. As our experimental results show, these new methods are potentially useful tools for signal processing with huge datasets since they provide fast algorithms for calculating low-rank approximations without needing to sample the whole dataset.

Original languageEnglish
Title of host publicationCAMSAP 2009 - 2009 3rd IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing
Pages73-76
Number of pages4
DOIs
Publication statusPublished - 2009
Externally publishedYes
Event2009 3rd IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, CAMSAP 2009 - Aruba, Netherlands
Duration: 13 Dec 200916 Dec 2009

Publication series

NameCAMSAP 2009 - 2009 3rd IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing

Conference

Conference2009 3rd IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, CAMSAP 2009
Country/TerritoryNetherlands
CityAruba
Period13/12/0916/12/09

Fingerprint

Dive into the research topics of 'Methods for factorization and approximation of tensors by partial fiber sampling'. Together they form a unique fingerprint.

Cite this