同时为了进一步降低存局部潜在语义分类的存储空间的开销，采用半离散分解方法替代奇异值分解方法。
Meanwhile to reduce the cost of memory space, this paper takes the Semi-Discrete Decomposition Method rather than the Singular Value Decomposition.
第一，提出一种有监督的潜在语义索引(SLSI)模型降维方法，用于文本分类任务中的特征表示。
The main contributions include: 1 a novel dimension reduction method, Supervised Latent Semantic Indexing SLSI, was proposed to represent documents for text classification tasks.
为了提高文本分类的准确性，研究并设计了一个基于潜在语义分析和支持向量机的多类文本分类模型。
A multiclass text categorization model based on latent semantic analysis and support vector machine is researched and designed to enhance the accuracy of categorization.
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