Given a classification problem, our goal is to find a low-dimensional linear transformation of the feature vectors which retains information needed to predict the class labels. We present a method based on maximum conditional likelihood estimation of mixture models. Use of mixture models allows us to approximate the distributions to any desired accuracy while use of conditional likelihood as the contrast function ensures that the selected subspace retains maximum possible mutual information between feature vectors and class labels. Classification experiments using Gaussian mixture components show that this method compares favorably to related dimension reduction techniques. Other distributions belonging to the exponential family can be used to reduce dimensions when data is of a special type, for example binary or integer valued data. We provide an EM-like algorithm for model estimation and present visualization experiments using both the Gaussian and the Bernoulli mixture models.
The authors of these documents have submitted their reports to this technical report series for the purpose of non-commercial dissemination of scientific work. The reports are copyrighted by the authors, and their existence in electronic format does not imply that the authors have relinquished any rights. You may copy a report for scholarly, non-commercial purposes, such as research or instruction, provided that you agree to respect the author's copyright. For information concerning the use of this document for other than research or instructional purposes, contact the authors. Other information concerning this technical report series can be obtained from the Computer Science and Engineering Department at the University of California at San Diego, email@example.com.
[ Search ]