Incremental learning algorithm based on graph regularized non-negative matrix factorization with sparseness constraints

doi:10.11772/j.issn.1001-9081.2017.04.1071

Journal of Computer Applications ›› 2017, Vol. 37 ›› Issue (4): 1071-1074.DOI: 10.11772/j.issn.1001-9081.2017.04.1071

Previous Articles Next Articles

Incremental learning algorithm based on graph regularized non-negative matrix factorization with sparseness constraints

WANG Jintao, CAO Yudong, SUN Fuming

School of Electronics and Information Engineering, Liaoning University of Technology, Jinzhou Liaoning 121001, China

Received:2016-08-09 Revised:2016-10-14 Online:2017-04-10 Published:2017-04-19
Supported by:
This work is partially supported by the National Natural Science Foundation of China (61572244), the Program for Liaoning Excelent Talents in Universities (LR2015030).

稀疏约束图正则非负矩阵分解的增量学习算法

汪金涛, 曹玉东, 孙福明

辽宁工业大学电子与信息工程学院, 辽宁锦州 121001

通讯作者: 曹玉东
作者简介:汪金涛(1992-),男,安徽合肥人,硕士研究生,主要研究方向:模式识别;曹玉东(1971-),男,辽宁铁岭人,副教授,博士,主要研究方向:图像处理、模式识别;孙福明(1972-),男,辽宁大连人,教授,博士,CCF会员,主要研究方向:机器学习、模式识别。
基金资助:
国家自然科学基金资助项目（61572244）；辽宁省高等学校优秀人才支持计划项目（LR2015030）。

Abstract

Abstract: Focusing on the issues that the sparseness of the data obtained after Non-negative Matrix Factorization (NMF) is reduced and the computing scale increases rapidly with the increasing of training samples, an incremental learning algorithm based on graph regularized non-negative matrix factorization with sparseness constraints was proposed. It not only considered the geometric structure in the data representation, but also introduced sparseness constraints to coefficient matrix and combined them with incremental learning. Using the results of previous factorization involved in iterative computation with sparseness constraints and graph regularization, the cost of the computation was reduced and the sparseness of data after factorization was highly improved. Experiments on both ORL and PIE face recognition databases demonstrate the effectiveness of the proposed method.

Key words: Non-negative Matrix Factorization (NMF), sparse constraint, graph regularization, geometry, incremental learning

摘要： 针对非负矩阵分解后数据的稀疏性降低、训练样本增多导致运算规模不断增大的现象，提出了一种稀疏约束图正则非负矩阵分解的增量学习算法。该方法不仅考虑数据的几何信息，而且对系数矩阵进行稀疏约束，并将它们与增量学习相结合。算法在稀疏约束和图正则化的条件下利用上一步的分解结果参与迭代运算，在节省大量运算时间的同时提高了分解后数据的稀疏性。在ORL和PIE人脸数据库上的实验结果表明了该算法的有效性。

关键词: 非负矩阵分解, 稀疏约束, 图正则, 几何结构, 增量学习

CLC Number:

TP181

WANG Jintao, CAO Yudong, SUN Fuming. Incremental learning algorithm based on graph regularized non-negative matrix factorization with sparseness constraints[J]. Journal of Computer Applications, 2017, 37(4): 1071-1074.

汪金涛, 曹玉东, 孙福明. 稀疏约束图正则非负矩阵分解的增量学习算法[J]. 计算机应用, 2017, 37(4): 1071-1074.

References

[1] LEE D D, SEUNG H S. Learning the parts of objects by non-negative matrix factorization[J]. Nature, 1999, 401(6755): 788-791.
[2] HOYER P O. Non-negative matrix factorization with sparseness constraints[J]. Journal of Machine Learning Research, 2004, 5(1): 1457-1469.
[3] SERHAT S. BUCAK, B GUNSEL. Incremental subspace learning via non-negative matrix factorization[J]. Pattern Recognition, 2009, 42(5): 788-797.
[4] 王万良, 蔡竞. 稀疏约束下非负矩阵分解的增量学习算法[J]. 计算机科学, 2014, 41(8): 241-244.(WANG W L, CAI J. Incremental learning algorithm of non-negative matrix factorization with sparseness constraints[J]. Computer Science, 2014, 41(8): 241-244.)
[5] LIU H, WU Z, LI X, et al. Constrained non-negative matrix factorization for image representation[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2012, 34(7): 1299-1311.
[6] CAI D, HE X, HAN J, et al. Graph regularized non-negative matrix factorization for data representation[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2011, 33(8): 1548-1560.
[7] 姜伟, 李宏, 于震国,等. 稀疏约束图正则非负矩阵分解[J]. 计算机科学, 2013, 40(1): 218-256.(JIANG W, LI H, YU Z G, et al. Graph regularized non-negative matrix factorization with sparseness constraints[J]. Computer Science, 2013, 40(1): 218-256.)
[8] 李乐, 章毓晋. 非负矩阵分解算法综述[J]. 电子学报, 2008, 36(4): 737-743.(LI L, ZHANG Y J. A survey on algorithms of non-negative matrix factorization[J]. Acta Electronica Sinica, 2008, 36(4): 737-743.)
[9] 胡丽莹, 郭躬德, 马昌凤. 基于对称非负矩阵分解的重叠社区发现方法[J]. 计算机应用, 2015, 35(10): 2742-2746.(HU L Y, GUO G D, MA C F. Overlapping community discovery method based on symmetric nonnegative matrix factorization[J]. Journal of Computer Applications, 2015, 35(10): 2742-2746.)
[10] 姜小燕. 基于基于非负矩阵分解的图像分类算法研究[D]. 锦州: 辽宁工业大学, 2016.(JIANG X Y. Research on image classification algorithm based on non-negative matrix factorization[D]. Jinzhou: Liaoning University of Technology, 2016.)
[11] 汪金涛, 曹玉东, 王梓宁,等. 图像型垃圾邮件监控系统研究与设计[J]. 辽宁工业大学学报(自然科学版), 2016, 36(2): 78-81.(WANG J T, CAO Y D, WANG Z N, et al. Research and design of monitoring system on image spam[J]. Journal of Liaoning University of Technology (Natural Science Edition), 2016, 36(2): 78-81.)
[12] 胡学考, 孙福明, 李豪杰. 基于稀疏约束的半监督非负矩阵分解算法[J]. 计算机科学, 2015, 42(7): 280-304.(HU X K, SUN F M. LI H J. Constrained nonnegative matrix factorization with sparseness for image representation[J]. Computer Science, 2015, 42(7): 280-304.)
[13] 杜世强, 石玉清, 王维兰,等. 基于图正则化的半监督非负矩阵分解[J]. 计算机工程与应用, 2012, 48(36): 194-200.(DU S Q, SHI Y Q, WANG W L, et al. Graph-regularized-based semi-supervised non-negative matrix factorization[J]. Computer Engineering and Applications, 2012, 48(36): 194-200.)

[1]	LUO Changyin, CHEN Xuebin, MA Chundi, WANG Junyu. Online federated incremental learning algorithm for blockchain [J]. Journal of Computer Applications, 2021, 41(2): 363-371.
[2]	DENG Wenhan, ZHANG Ming, WANG Lijin, ZHONG Yiwen. Hybrid population-based incremental learning algorithm for solving closed-loop layout problem [J]. Journal of Computer Applications, 2021, 41(1): 95-102.
[3]	LIU Dan, YAO Lishuang, WANG Yunfeng, PEI Zuofei. Classification model for class imbalanced traffic data [J]. Journal of Computer Applications, 2020, 40(8): 2327-2333.
[4]	GUAN Hao, QIN Xiaolin, RAO Yongsheng, CAO Sheng. Domain model of Web-based dynamic geometry software and its applications [J]. Journal of Computer Applications, 2020, 40(4): 1127-1132.
[5]	LUO Keyun, YE Baoliu, TANG Bin, MEI Feng, LU Wenda. Incremental learning based proactive caching mechanism for RocksDB key-value system [J]. Journal of Computer Applications, 2020, 40(2): 321-327.
[6]	LIU Ying, LIANG Nannan, LI Daxiang, YANG Fanchao. Hyperspectral image unmixing algorithm based on spectral distance clustering [J]. Journal of Computer Applications, 2019, 39(9): 2541-2546.
[7]	CHEN Shanxue, CHU Chengquan. Hyperspectral unmixing based on sparse and orthogonal constrained non-negative matrix factorization [J]. Journal of Computer Applications, 2019, 39(8): 2276-2280.
[8]	LONG Yanshan, FU Qinxue, GUO Jibin, ZHANG Mengqi, CAI Yueming. Successful offloading probability analysis in device-to-device caching network based on stochastic geometry [J]. Journal of Computer Applications, 2019, 39(8): 2346-2353.
[9]	YANG Liangdong, YANG Zhixia. Incremental robust non-negative matrix factorization with sparseness constraints and its application [J]. Journal of Computer Applications, 2019, 39(5): 1275-1281.
[10]	YU Jianglan, LI Xiangli, ZHAO Pengfei. Sparse non-negative matrix factorization based on kernel and hypergraph regularization [J]. Journal of Computer Applications, 2019, 39(3): 742-749.
[11]	CHEN Ping, GONG Xun. Face alignment method based on scale self-adaption and incremental learning [J]. Journal of Computer Applications, 2018, 38(7): 2064-2069.
[12]	LONG Yanshan, WU Dan, CAI Yueming, WANG Meng, GUO Jibin. Cache placement optimization scheme in D2D networks with heterogeneous cache capacity [J]. Journal of Computer Applications, 2018, 38(5): 1453-1457.
[13]	HU Lisha, WANG Suzhen, CHEN Yiqiang, HU Chunyu, JIANG Xinlong, CHEN Zhenyu, GAO Xingyu. Objective equilibrium measurement based kernelized incremental learning method for fall detection [J]. Journal of Computer Applications, 2018, 38(4): 928-934.
[14]	JIANG Maosong, WANG Dongxia, NIU Fanglin, CAO Yudong. Speech enhancement method based on sparsity-regularized non-negative matrix factorization [J]. Journal of Computer Applications, 2018, 38(4): 1176-1180.
[15]	SUN Shengzi, WAN Yuan, ZENG Cheng. Semi-supervised adaptive multi-view embedding method for feature dimension reduction [J]. Journal of Computer Applications, 2018, 38(12): 3391-3398.

Incremental learning algorithm based on graph regularized non-negative matrix factorization with sparseness constraints

稀疏约束图正则非负矩阵分解的增量学习算法

PDF

Knowledge

Abstract

Cite this article

share this article

References

Related Articles 15

Recommended Articles

Metrics