Fast nonnegative tensor factorization by using accelerated proximal gradient

Guoxu Zhou, Qibin Zhao, Yu Zhang, Andrzej Cichocki

Результат исследований: Вклад в журналСтатьярецензирование

4 Цитирования (Scopus)

Аннотация

Nonnegative tensor factorization (NTF) has been widely applied in high-dimensional nonnegative tensor data analysis. However, existing algorithms suffer from slow convergence caused by the nonnegativity constraint and hence their practical applications are severely limited. By combining accelerated proximal gradient and low-rank approximation, we propose a new NTF algorithm which is significantly faster than state-of-the-art NTF algorithms.

Язык оригиналаАнглийский
Страницы (с-по)459-468
Число страниц10
ЖурналLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Том8866
DOI
СостояниеОпубликовано - 2014
Опубликовано для внешнего пользованияДа

Fingerprint

Подробные сведения о темах исследования «Fast nonnegative tensor factorization by using accelerated proximal gradient». Вместе они формируют уникальный семантический отпечаток (fingerprint).

Цитировать