"Learning low dimensional representations of high dimensional data"

Fei Sha, University of Pennsylvania

...In this talk, I will focus on my research that have brought new and interesting developments into the frameworks of NMF and LDA. In the first project, I show how to extend the original NMF approach to learning meaningful "audio parts" from speech and audio data. The audio parts robustly encode harmonic structures in the voices, which are key acoustic features for building machines that can analyze complicated acoustic signals as well as human listeners. In the second project, I investigate how to incorporate supervisory information like class labels in LDA models. In the supervised LDA, topics are discovered by grouping words based on not only semantic similarity but also class label proximity. These topics yield compact representation with better predictive powers than those derived from the original unsupervised LDA...