Text Dimensionality Reduction with Mutual Information Preserving Mapping

With the explosion of information, it is becoming increasingly difficult to get what is really wanted. Dimensionality reduction is the first step in efficient processing of large data. Although dimensionality can be reduced in many ways, little work has been done to achieve dimensionality reduction without changing the inner semantic relationship among high dimension data. To remedy this problem, we introduced a manifold learning based method, named Mutual information preserving mapping (MIPM), to explore the low-dimensional, neighborhood and mutual information preserving embeddings of highdimensional inputs. Experimental results show that the proposed method is effective for the text dimensionality reduction task. The MIPM was used to develop a temporal summarization system for efficiently monitoring the information associated with an event over time. With respect to the established baselines, results of these experiments show that our method is effective in the temporal summarization.