Dimensionality reduction by combining category information and latent semantic index for text categorization

作者：Zheng Wenbin^*; An Lixin; Xu Zhanyi

来源：Journal of Information and Computational Science, 2013, 10(8): 2463-2469.

DOI：10.12733/jics20101814

• Benchmark data
• Category information
• Dimensionality reduction
• Dimensionality reduction method
• Latent Semantic Indexing
• Latent semantic indices
• Optimal classification
• Text categorization

The Latent Semantic Indexing (LSI) is a commonly used dimensionality reduction methods in text categorization; however, as a linear reconstructed method, its goal is to obtain the optimal representative feature rather than the optimal classification feature. This paper proposes a novel method in which the categorization information is combined into the latent semantic indexing to obtain more discriminating features than the standard latent semantic indexing. The experimental results show that the proposed method achieves good performance on two benchmark data sets, especially in the case where the dimensionality is greatly reduced.

• 出版日期2013
• 单位中国计量大学