SLOW FEATURES NONNEGATIVE MATRIX FACTORIZATION FOR TEMPORAL DATA DECOMPOSITION

被引：0

作者：

Zafeiriou, Lazaros ^{[1
]}

Nikitidis, Symeon ^{[1
]}

Zafeiriou, Stefanos ^{[1
]}

Pantic, Maja ^{[1
,2
]}

机构：

[1] Imperial Coll London, Dept Comp, London, England

[2] Univ Twente, EEMCS, NL-7500 AE Enschede, Netherlands

来源：

2014 IEEE INTERNATIONAL CONFERENCE ON IMAGE PROCESSING (ICIP) | 2014年

基金：

英国工程与自然科学研究理事会;

关键词：

Nonnegative Matrix Factorization; Slow Features Analysis; Facial behaviour dynamics analysis;

D O I：

暂无

中图分类号：

TP301 [理论、方法];

学科分类号：

081202 ;

摘要：

In this paper, we combine the principles of temporal slowness and nonnegative parts-based learning into a single framework that aims to learn slow varying parts-based representations of time varying sequences. We demonstrate that the proposed algorithm arises naturally by embedding the Slow Features Analysis trace optimization problem in the nonnegative subspace learning framework and derive novel multiplicative update rules for its optimization. The usefulness of the developed algorithm is demonstrated for unsupervised facial behaviour dynamics analysis on MMI database.

引用

页码：1430 / 1434

页数：5

共 18 条

[1]

[Anonymous], 2003, P NEUR INF PROC SYST

[2] Graph Regularized Nonnegative Matrix Factorization for Data Representation [J].