A Supervised Tensor Dimension Reduction-Based Prognostics Model for Applications with Incomplete Imaging Data

This paper proposes a supervised dimension reduction methodology for tensor data which has two advantages over most image-based prognostic models. First, the model does not require tensor data to be complete which expands its application to incomplete data. Second, it utilizes time-to-failure (TTF) to supervise the extraction of low-dimensional features which makes the extracted features more effective for the subsequent prognostic. Besides, an optimization algorithm is proposed for parameter estimation and closed-form solutions are derived under certain distributions.Comment: 42 pages, 17 figure

Adjective Density as a Text Formality Characteristic for Automatic Text Classification: A Study Based on the British National Corpus

PACLIC 23 / City University of Hong Kong / 3-5 December 200

Latin Etymologies as Features on BNC Text Categorization

PACLIC 23 / City University of Hong Kong / 3-5 December 200