Abstract
Feather Selection is an effective method to reduce the dimension of text feature. The existing feature selection methods usually use empirical estimation methods when determining the scale of the feature selection. These methods have achieved good results in some specific corpora. However, it is not easy to promote the further generalization of automatic text categorization due to the insufficient theoretical basis. Therefore, a text feature selection algorithm based on Chi-square rank correlation factorization is proposed based on the comprehensive consideration of the whole and local distribution of text features. Under the condition that the algorithm does not need any prior knowledge, the feature weights are portrayed and the feature selection is completed, fully reflects the characteristics of the probability distribution.
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