Non-negative tensor factorization based on feedback sparse constraints

Journal of Computer Applications ›› 2013, Vol. 33 ›› Issue (10): 2871-2873.

• Multimedia processing technology • Previous Articles Next Articles

Non-negative tensor factorization based on feedback sparse constraints

LIU Yanan¹,²,XU Zhengzheng²,LUO Bin²

1. Department of Computer Science and Technology, Hefei Normal College, Hefei Anhui 230601, China
2. School of Computer Science and Technology, Anhui University, Hefei Anhui 230039, China;

Received:2013-04-15 Revised:2013-05-29 Online:2013-11-01 Published:2013-10-01
Contact: LUO Bin

基于反馈稀疏约束的非负张量分解算法

刘亚楠¹,²,涂铮铮¹,罗斌¹

1. 安徽大学计算机科学与技术学院, 合肥 230039
2. 合肥师范学院计算机科学与技术系,合肥 230601

通讯作者: 罗斌
作者简介:刘亚楠(1984-)，女，山东济宁人，讲师，博士研究生，主要研究方向：模式识别、图像处理；涂铮铮(1982-)，女，安徽六安人，讲师，博士研究生，主要研究方向：模式识别、图像处理、视频信号处理；罗斌(1963-)，男，安徽合肥人，教授，博士生导师，主要研究方向：模式识别、图像处理。
基金资助:
国家自然科学基金资助项目;高校省级优秀青年人才基金重点资助项目;安徽大学“211”工程创新团队项目

Abstract

Abstract: In order to fully use the structural information of the data, and compress the image data, the sparse constraints of the subspace (feedback) were applied to the object function of non-negative tensor factorization. Then this algorithm was used to reduce the dimension of the image sets. Finally, image classification was realized. The experimental results on the handwritten digital image database show that the proposed algorithm can effectively improve the accuracy of the image classification.

Key words: Non-negative Matrix Factorization (NMF), sparse constraint, tensor factorization

摘要： 为了充分利用图像本身的结构信息并充分压缩图像数据，把得到的子空间中数据(反馈)的稀疏性作为约束项加入非负张量分解目标函数中，即采用基于反馈稀疏约束的非负张量分解算法对图像集合进行降维。最后，将该算法应用于手写数字图像库中，实验结果表明所提出的方法能有效改善图像分类的准确性

关键词: 非负矩阵分解, 稀疏约束, 张量分解

CLC Number:

TP391.41

LIU Yanan XU Zhengzheng LUO Bin. Non-negative tensor factorization based on feedback sparse constraints[J]. Journal of Computer Applications, 2013, 33(10): 2871-2873.

刘亚楠涂铮铮罗斌. 基于反馈稀疏约束的非负张量分解算法[J]. 计算机应用, 2013, 33(10): 2871-2873.

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Non-negative tensor factorization based on feedback sparse constraints

基于反馈稀疏约束的非负张量分解算法

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