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Nie, F., Huang, Y., Wang, X. and Huang, H. (2014) New Primal SVM Solver with Linear Computational Cost for Big Data Classifications. Proceedings of the 31st International Conference on Machine Learning, 32, 505-513.

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• TITLE: A New Algorithm for Reducing Dimensionality of L1-CSVM Use Augmented Lagrange Method

  AUTHORS: Mingzhu Cui, Liya Fan

  KEYWORDS: Support Vector Machine, Dimensionality Reduction, Augmented Lagrange Method, Classification

  JOURNAL NAME: Journal of Applied Mathematics and Physics, Vol.10 No.1, January 11, 2022

  ABSTRACT: Principal component analysis and generalized low rank approximation of matrices are two different dimensionality reduction methods. Two different dimensionality reduction algorithms are applied to the L1-CSVM model based on augmented Lagrange method to explore the variation of running time and accuracy of the model in dimensionality reduction space. The results show that the improved algorithm can greatly reduce the running time and improve the accuracy of the algorithm.