Semantic Subspace Learning with conditional significance vectors

Subspace detection and processing is receiving more attention nowadays as a method to speed up search and reduce processing overload. Subspace Learning algorithms try to detect low dimensional subspaces in the data which minimize the intra-class separation while maximizing the inter-class separation. In this paper we present a novel technique using the maximum significance value to detect a semantic subspace. We further modify the document vector using conditional significance to represent the subspace. This enhances the distinction between classes within the subspace. We compare our method against TFIDF with PCA and show that it consistently outperforms the baseline with a large margin when tested with a wide variety of learning algorithms. Our results show that the combination of subspace detection and conditional significance vectors improves subspace learning.