Information Filtering with Extracted Index Words Using ICA

We propose an information filtering system with extracted index words using by Independent Component Analysis(ICA). Elements of a document vector are established as the weights of index words and their dimensions become larger as the number of documents is increased. Therefore, from the view point of processing time and memory space, the dimension must be decreased. The proposed method decreases the dimension by selecting the index words based on the topics included in the corpus. We have applied ICA to the documents to obtain the topics. Then filtering by the relevance feedback with the document vectors reconstructed by the selected index words, was carried out to confirm the effectiveness of the proposed method.