Subspace Mapping of Noisy Text Documents

Abstract. Subspace mapping methods aim at projecting high-dimensional data into a subspace where a specific objective function is optimized. Such dimension reduction allows the removal of collinear and irrelevant variables for creating informative visualizations and task-related data spaces. These specific and generally de-noised subspaces spaces enable machine learning methods to work more efficiently. We present a new and general subspace mapping method, Correlative Matrix Mapping (CMM), and evaluate its abilities for category-driven text organization by assessing neighborhood preservation, class coherence, and classification. This approach is evaluated for the challenging task of processing short and noisy documents.

• Type: Journal
• Year: 2011
• Where: AI
• Authors: Axel J. Soto, Marc Strickert, Gustavo E. Vazquez, Evangelos E. Milios
• Keywords: AI 2011, Artificial Intelligence, Data Spaces, Irrelevant Variables, Neighborhood Preservation