Nonnegative Shifted Tensor Factorization in time frequency domain

Qiang Wu, Ju Liu, Fengrong Sun, Jie Li, Andrzej Cichocki

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

Abstract

In this paper, we proposed a Nonnegative Shifted Tensor Factorization (NSTF) model considering multiple component delays by time frequency analysis. Explicit mathematical representation for the delays is presented to recover the patterns from the original data. In order to explore multilinear shifted component in different modes, we use fast fourier transform (FFT) to transform the non-integer delays into frequency domain by gradients search. The ALS algorithm for NSTF is developed by alternating least square procedure to estimate the nonnegative factor matrices in each mode and enforce the sparsity of model. Simulation results indicate that ALS-NSTF algorithm can extract the shift-invariance sparse features and improve the recognition performance of robust speaker identification and structural magnetic resonance imaging (sMRI) diagnosis for Alzheimer's Disease.

Original languageEnglish
Title of host publicationProceedings of the International Joint Conference on Neural Networks
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages3009-3014
Number of pages6
ISBN (Electronic)9781479914845
DOIs
Publication statusPublished - 3 Sep 2014
Externally publishedYes
Event2014 International Joint Conference on Neural Networks, IJCNN 2014 - Beijing, China
Duration: 6 Jul 201411 Jul 2014

Publication series

NameProceedings of the International Joint Conference on Neural Networks

Conference

Conference2014 International Joint Conference on Neural Networks, IJCNN 2014
Country/TerritoryChina
CityBeijing
Period6/07/1411/07/14

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