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

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...

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Vydáno v:Ji suan ji ke xue Ročník 51; číslo 2; s. 100 - 106
Hlavní autoři: Huang, Lulu, Tang, Shuyu, Zhang, Wei, Dai, Xiangguang
Médium: Journal Article
Jazyk:čínština
Vydáno: Chongqing Guojia Kexue Jishu Bu 01.02.2024
Editorial office of Computer Science
Témata:
Algorithms
Clustering
Data compression
Effectiveness
Error analysis
Euclidean geometry
Factorization
Feature extraction
Image compression
non-negative matrix factorization|lp-norm|clustering|parallel optimization|rate of convergence
Particle swarm optimization
Reconstruction
ISSN:1002-137X
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Shrnutí: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 MajorizationMinimization 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
Bibliografie:ObjectType-Article-1
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ISSN:1002-137X
DOI:10.11896/jsjkx.230300040