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An Efficient Discriminant Analysis Algorithm for Document Classification

机译：一种有效的文档分类判别分析算法

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Document categorization has become one of the most important research areas of pattern recognition and data mining due to the exponential growth of documents in the Internet and the emergent need to organize them. The document space is always of very high dimensionality and learning in such a high dimensional space is often impossible due to the curse of dimensionality. To cope with performance and accuracy problems with high dimensionality, a novel dimensionality reduction algorithm called IKDA is proposed in this paper. The proposed IKDA algorithm combines kernel-based learning techniques and direct iterative optimization procedure to deal with the nonlinearity of the document distribution. The proposed algorithm also effectively solves the so-called “small sample size” problem in document classification task. Extensive experimental results on two real world data sets demonstrate the effectiveness and efficiency of the proposed algorithm.
机译：由于Internet中文档的指数增长以及对组织它们的迫切需求，文档分类已成为模式识别和数据挖掘的最重要研究领域之一。文档空间始终具有很高的维数，由于维数的诅咒，在如此高维的空间中学习通常是不可能的。为了解决高维性能和精度问题，提出了一种新的降维算法IKDA。所提出的IKDA算法结合了基于内核的学习技术和直接的迭代优化程序，以处理文档分布的非线性问题。所提出的算法还有效地解决了文档分类任务中所谓的“小样本量”问题。在两个真实世界的数据集上的大量实验结果证明了该算法的有效性和效率。

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