Decomposition forward SVM dimension-reduction algorithm based on independent component analysis
基于独立成分分析的分解向前SVM降维算法

A Decomposition Forward Support Vector Machine (DFSVM) algorithm for large scale samples learning and a new dimension reduction model based on Independent Component Analysis (ICA) were proposed. The calculational complexity is lower than that of the traditional chunking algorithm and the standard SVM. Use the idea of imcomplete ICA to compress data and reduce the dimension. Because of the reduced input dimension and simplified data structure, the calculational complexity of SVM has been reduced. Experiment indicates that if reducing the dimension from one hundred and ten dimension to five-dimension, the average recognition rate is superior to traditional neural network and comes to 93%. Considering the time cost and the recognition efficiency, ICA dimension reduction model is an ideal application model in practice.