Hybrid linear matrix factorization for topic-coherent terms clustering

We propose a novel Karhunen-Loeve Transformation (KLT) for dimension reduction.Karhunen-Loeve expansion based on Wiener process on KLT results for optimization.State-of-the-art topic-coherence metrics are used for word clustering and evaluation. Topic-coherent term clustering is the foundation of document organization, corpus summarization and document classification. It is especially useful in solving the emerging problem of big data. However, a term clustering method that can cope with high-dimension data with variable length and topics and meanwhile achieve high topic coherence is an ongoing request. It is a challenging problem in research. This paper proposes a hybrid linear matrix factorization method to identify the topic-coherent terms from documents to form a thesaurus for clustering. Starting from an analog Karhunen-Loeve transformation from PCA scores fully into FA's factor coefficients space (loadings), the high-dimension of the full set of PCA scores is reduced and topic-coherent terms are classified by the main factors of FA which could be topics. Karhunen-Loeve transformation reduces the total mean square error to increase topic coherence. The optimization of the initial transformation is carried out further in a manner of Karhunen-Loeve expansion based on stochastic Wiener process. The optimal topic coherent bags of terms are found to build a more topic-coherent model. This approach is experimented on the CISI, MedSH and Tweets dataset in different sizes and number of topics. It achieves outstanding results better than the methods in comparison.