Nonnegative tensor train decompositions for multi-domain feature extraction and clustering

Namgil Lee, Anh Huy Phan, Fengyu Cong, Andrzej Cichocki

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12 Цитирования (Scopus)

Аннотация

Tensor train (TT) is one of the modern tensor decomposition models for low-rank approximation of high-order tensors. For nonnegative multiway array data analysis, we propose a nonnegative TT (NTT) decomposition algorithm for the NTT model and a hybrid model called the NTT-Tucker model. By employing the hierarchical alternating least squares approach, each fiber vector of core tensors is optimized efficiently at each iteration. We compared the performances of the proposed method with a standard nonnegative Tucker decomposition (NTD) algorithm by using benchmark data sets including event-related potential data and facial image data in multi-domain feature extraction and clustering tasks. It is illustrated that the proposed algorithm extracts physically meaningful features with relatively low storage and computational costs compared to the standard NTD model.

Язык оригиналаАнглийский
Название основной публикацииNeural Information Processing - 23rd International Conference, ICONIP 2016, Proceedings
РедакторыAkira Hirose, Minho Lee, Derong Liu, Kenji Doya, Kazushi Ikeda, Seiichi Ozawa
ИздательSpringer Verlag
Страницы87-95
Число страниц9
ISBN (печатное издание)9783319466743
DOI
СостояниеОпубликовано - 2016
Опубликовано для внешнего пользованияДа

Серия публикаций

НазваниеLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Том9949 LNCS
ISSN (печатное издание)0302-9743
ISSN (электронное издание)1611-3349

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