Computer Science

Computer Science ›› 2024, Vol. 51 ›› Issue (2): 100-106.doi: 10.11896/jsjkx.230300040

• Database & Big Data & Data Science • Previous Articles     Next Articles

Non-negative Matrix Factorization Parallel Optimization Algorithm Based on Lp-norm

HUANG Lulu1, TANG Shuyu2, ZHANG Wei 3, DAI Xiangguang3   

  1. 1 School of Electronics and Information Engineering,Chongqing Three Gorges University,Chongqing 404100,China
    2 School of Computer Science and Engineering,Chongqing Three Gorges University,Chongqing 404100,China
    3 Key Laboratory of Intelligent Information Processing and Control,Chongqing Three Gorges University,Wanzhou,Chongqing 404100,China
  • Received:2023-03-06 Revised:2023-06-15 Online:2024-02-15 Published:2024-02-22
  • About author:HUANG Lulu,born in 1997,postgra-duate.Her main research interests include computer vision and algorithm optimization.ZHANG Wei,born in 1970,Ph.D,professor.His main research interests include information security and computational intelligence.
  • Supported by:
    Science and Technology Research Project of Chongqing Municipal Education Commission(KJZD-M202201204,KJZD-K202201205) and Science and Technology Innovation Smart Agriculture Project of Science and Technology Department,Wanzhou District of Chongqing(2022-17).

PDF (PC)

987

Abstract: Non-negative matrix factorization algorithm is an important tool for image clustering,data compression and feature extraction.Traditional non-negative matrix factorization algorithms mostly use Euclidean distance to measure reconstruction error,which has shown its effectiveness in many tasks,but still has the problems of suboptimal clustering results and slow convergence.To solve these problems,the loss function of non-negative matrix factorization is reconstructed by Lp-norm to obtain better clustering results by adjusting the coefficient p.Based on the collaborative optimization theory and Majorization-Minimization algorithm,this paper uses the particle swarm optimization to solve the non-negative matrix factorization problem of reconstruction in parallel.The feasibility and effectiveness of the proposed method is verified in real datasets,and the experimental results show that the proposed algorithm significantly improves program execution efficiency and outperforms the traditional non-negative matrix decomposition algorithm in a series of evaluation metrics.

Key words: Non-negative matrix factorization, Lp-norm, Clustering, Parallel optimization, Rate of convergence

CLC Number: 

  • TP391
[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]ADDI H,WILLIAMS L J.Principal component analysis[J].Wiley Interdisciplinary Reviews:Computational Statistics,2010,2(4):433-459.
[3]PAATEROP P,TAPPER U.Positive matrix factorization:Anon-negative factor model with optimal utilization of error estimates of data values[J].Environmetrics,1994,5(2):111-126.
[4]LI X L,JIA M X.Nonnegative Matrix Factor-ization Algorithmwith Hypergraph Based on pertreatments[J].Computer Science,2020,47(7):71-77.
[5]JAIN A K,DUIN R P W,MAO J.Statistical pattern recognition:A review[J].IEEE Transactions on pattern analysis and machine intelligence,2000,22(1):4-37.
[6]MITCHELL T,BUCHANAN B,DEJONG G,et al.Machinelearning[J].Annual Review of Computer Science,1990,4(1):417-433.
[7]GUAN N,TAO D,LUO Z,et al.MahNMF:Manhattan Non-negative Matrix Factorization[J].Journal of Machine Learning Research,2012,1050:14-59.
[8]NESTEROV Y.Smooth minimization of non-smooth functions[J].Mathematical Programming,2005,103(1):127-152.
[9]DING C,HE X,SIMON H D.On the equivalence of non-negative matrix factorization and spectral clustering[C]//Procee-dings of the 2005 SIAM International Conference on Data Mi-ning.Society for Industrial and Applied Mathematics,2005:606-610.
[10]WANG S,CHANG T H,CUI Y,et al.Clustering by orthogonal NMF model and non-convex penalty optimization[J].IEEE Transactions on Signal Processing,2021,69:5273-5288.
[11]NAGAYAMA M,ARITAKE T,HINO H,et al.Detecting cell assemblies by NMF-based clustering from calcium imaging data[J].Neural Networks,2022,149:29-39.
[12]KONG D,DING C,HUANG H.Robust non-negative matrixfactorization using L2,1-norm[C]//The 20th ACM InternationalConference on Information and Knowledge Management.2011:673-682.
[13]HOYER P O.Non-negative matrix factorization with sparseness constraints[J].Journal of Machine Learning Research,2004,5(9):1457-1469.
[14]NAKAMURA T,KAMEOKA H.Lp-norm non-negative matrix factorization and its application to singing voice enhancement[C]//2015 IEEE International Conference on Acoustics,Speech and Signal Processing(ICASSP).IEEE,2015:2115-2119.
[15]ZHANG L,CHEN Z,ZHENG M,et al.Robust non-negativematrix factorization[J].Frontiers of Electrical and Electronic Engineering in China,2011,6:192-200.
[16]LANGE K.MM optimization algorithms[M].Society for Industrial and Applied Mathematics,2016.
[17]YAO Y,LI Z,LIU H,et al.Robust Transceiver OptimizationAgainst Echo Eclipsing Via Majorization-Minimization[J].IEEE Transactions on Aerospace and Electronic Systems,2022,59(3):2464-2479.
[18]PANWAR K,KATWE M,BABU P,et al.A Majorization-Minimization Algorithm for Hybrid T-OA-RSS Based Localization in NLOS Environment[J].IEEE Communications Letters,2022,26(5):1017-1021.
[19]LIU F,SHANG X,ZHU H.Efficient majorization-minimization-based channel estimation for onebit massive MIMO systems[J].IEEE Transactions on Wireless Communications,2021,20(6):3444-3457.
[20]XU J,LANGE K.Power k-means clustering[C]//International Conference On Machine Learning. PMLR,2019:6921-6931.
[21]NESTEROV Y E.A method for solving the convex program-ming problem with convergence rate O(1/k∧2) [J].Dokl.Akad.Nauk SSSR269,1983,269(3):543-547.
[22]QU L C,LYU J,QU Y H,et al.Intelligent Assignment and Positioning Algorithm of Moving Target Based on Fuzzy Neural Network[J].Computer Science,2021,48(8):246-252.
[23]YU M,LI G,JIANG D,et al.Application of PSO-RBF neuralnetwork in gesture recognition of continuous surface EMG signals[J].Journal of Intelligent & Fuzzy Systems,2020,38(3):2469-2480.
[24]QABOUCHE H,SAHEL A,BADRI A,et al.Energy efficient PSO-based routing protocol for large WSN[C]//2022 2nd International Conference on Innovative Research in Applied Science,Engineering and Technology(IRASET). IEEE,2022:1-7.
[25]LI Y.An Improved Particle Swarm Optimization AlgorithmBased on Simulated Annealing for Large-Scale Node Location of WSN[C]//2022 IEEE 4th International Conference on Civil Aviation Safety and Information Technology(ICCASIT).IEEE,2022:971-974.
[26]ALHAJ Y A,DAHOU A,ALQANESS M A,et al.A novel text classification technique using im-proved particle swarm optimization:A case study of Arabic language[J].Future Internet,2022,14(7):194-211.
[27]WANG L Z,MU X D,LIU H L.Using SVM Method Optimized by Improved Particle Swarm O-ptimization to Analyze Emotion of Chinese Text[J].Computer Science,2020,47(1):231-236.
[28]PAN Y,WU Y,LAM H K.Security based fuzzy control for nonlinear networked control systems with DoS attacks via a resilient event-triggered scheme[J].IEEE Transactions on Fuzzy Systems,2022,30(10):4359-4368.
[29]LEE D D,SEUNGH S.Algorithms for non-negative matrix factorization[J].Advances in Neural Information Processing Systems,2000,13:556-562.
[1] WANG Hancheng, DAI Haipeng, CHEN Zhipeng, CHEN Shusen, CHEN Guihai. Large-scale Network Community Detection Algorithm Based on MapReduce [J]. Computer Science, 2024, 51(4): 11-18.
[2] KANG Wei, LI Lihui, WEN Yimin. Semi-supervised Classification of Data Stream with Concept Drift Based on Clustering Model Reuse [J]. Computer Science, 2024, 51(4): 124-131.
[3] QIAO Fan, WANG Peng, WANG Wei. Multivariate Time Series Classification Algorithm Based on Heterogeneous Feature Fusion [J]. Computer Science, 2024, 51(2): 36-46.
[4] YANG Bo, LUO Jiachen, SONG Yantao, WU Hongtao, PENG Furong. Time Series Clustering Method Based on Contrastive Learning [J]. Computer Science, 2024, 51(2): 63-72.
[5] GUO Shangzhi, LIAO Xiaofeng, XIAN Kaiyi. Logical Regression Click Prediction Algorithm Based on Combination Structure [J]. Computer Science, 2024, 51(2): 73-78.
[6] XU Jie, WANG Lisong. Contrastive Clustering with Consistent Structural Relations [J]. Computer Science, 2023, 50(9): 123-129.
[7] HU Shen, QIAN Yuhua, WANG Jieting, LI Feijiang, LYU Wei. Super Multi-class Deep Image Clustering Model Based on Contrastive Learning [J]. Computer Science, 2023, 50(9): 192-201.
[8] LIU Xiang, ZHU Jing, ZHONG Guoqiang, GU Yongjian, CUI Liyuan. Quantum Prototype Clustering [J]. Computer Science, 2023, 50(8): 27-36.
[9] CUI Yunsong, WU Ye, XU Xiaoke. Decoupling Analysis of Network Structure Affecting Propagation Effect [J]. Computer Science, 2023, 50(7): 368-375.
[10] WANG Mingxia, XIONG Yun. Disease Diagnosis Prediction Algorithm Based on Contrastive Learning [J]. Computer Science, 2023, 50(7): 46-52.
[11] LIU Yao, GUAN Lihe. Superpixel Segmentation Iterative Algorithm Based on Ball-k-means Clustering [J]. Computer Science, 2023, 50(6A): 220600114-7.
[12] WANG Zhen, YANG Zhengwei, GAO Shunqi, ZHANG Lei. Visualization of Ocean Data Vector Field Based on Streamline Distance Clustering [J]. Computer Science, 2023, 50(6A): 220300284-7.
[13] LUO Ruiqi, YAN Jinlin, HU Xinrong, DING Lei. EEG Emotion Recognition Based on Multiple Directed Weighted Graph and ConvolutionalNeural Network [J]. Computer Science, 2023, 50(6A): 220600128-8.
[14] CHEN Jie. Study on Long Text Topic Clustering Based on Doc2Vec Enhanced Features [J]. Computer Science, 2023, 50(6A): 220800192-6.
[15] ZHANG Peng, LI Xiaolin, WANG Liyan. Dynamic Neighborhood Density Clustering Algorithm Based on DBSCAN [J]. Computer Science, 2023, 50(6A): 220400127-7.
Viewed
Full text


Abstract

Cited
  Shared   
  Discussed   
No Suggested Reading articles found!
渝ICP备16013121号-4
Add:18# Honghu West Rd., Yubei ,Chongqing,China    Post Code: 401121
Tel: 023-63500828/023-67039612/023-67039623
E-mail: jsjkx12@163.com
Powered by Beijing Magtech Co., Ltd.