Semi-sparse algorithm based on multi-layer optimization for recommendation system
Proceedings of the 2012 International Workshop on Programming Models and Applications for Multicores and Manycores
Wrappers for web access logs feature selection
Proceedings of the 2nd International Conference on Web Intelligence, Mining and Semantics
Semi-sparse algorithm based on multi-layer optimization for recommender system
The Journal of Supercomputing
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Sequential Minimal Optimization (SMO) is one of simple but fast iterative algorithm for Support Vector Machine (SVM), while there is a large amount of vector multiplication in SMO, which is still expensive and time-consuming. In this paper, we propose our Semi-sparse algorithm to enhance the vector multiplication in the SMO algorithms for large-scale sparse matrices. In the worst scenario, the traditional sparse algorithm on SMO needs O(n1+n2) times of judgments and addressing on two sparse vectors which own m and n elements respectively, while Semi-sparse algorithm can nearly finish this multiplying process within O(n2). Our experimental results on two benchmarks show that the modified SVMTorch based on our Semi-sparse algorithm can perform significantly faster than SVMTorch based on the original sparse algorithm.