Exploring Latent Structure in Data Algorithms and Implementations


School of Electrical, Computer and Energy Engineering

PhD Final Oral Defense

Exploring Latent Structure in Data: Algorithms and Implementations by

Prasanna Sattigeri

November 14, 2014

3:00 PM

GWC 208


Dr. Andreas Spanias (chair)

Dr. Trevor Thornton

Dr. Michael Goryll

Dr. Konstantinos Tsakalis


Feature representation for raw data is one of the most important ingredients in a machine learning system. Hand designing features is an expensive and time consuming process.

Furthermore, they do not generalize well to unseen data and novel tasks. This dissertation focuses on building data-driven unsupervised models for analyzing raw data and developing efficient feature representations. Simultaneous segmentation and feature extraction approaches for silicon-pores sensor data are considered. Algorithms to improve transform domain features for ion-channel time-series signals based on matrix completion are presented. The improved features achieve better performance in classification tasks and in reducing the false alarm rates when applied to analyte detection. We will also present a new framework for feature extraction for challenging natural environment sounds and images. Several algorithms are proposed that perform supervised tasks such as recognition and tag annotation. Several strategies to speed-up

Orthogonal Matching Pursuit algorithm using CUDA kernel on a GPU are proposed.