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Exploring Latent Structure in Data: Algorithms and Implementations

Abstract Feature representations for raw data is one of the most important component in a machine learning system. Traditionally, features are \textit{hand crafted} by domain experts which can often be a time consuming process. Furthermore, they do not generalize well to unseen data and novel tasks. Recently, there have been many efforts to generate data-driven representations using clustering and sparse models. 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. Aggregating data into a matrix and performing low rank and sparse matrix decompositions with ... (more)
Created Date 2014
Contributor Sattigeri, Prasanna (Author) / Spanias, Andreas (Advisor) / Thornton, Trevor (Committee member) / Goryll, Michael (Committee member) / Tsakalis, Konstantinos (Committee member) / Arizona State University (Publisher)
Subject Electrical engineering / Computer engineering / Artificial intelligence / Feature Learning / GPU / Machine Learning / Retrieval / Sparse Coding
Type Doctoral Dissertation
Extent 147 pages
Language English
Reuse Permissions All Rights Reserved
Note Doctoral Dissertation Electrical Engineering 2014
Collaborating Institutions Graduate College / ASU Library
Additional Formats MODS / OAI Dublin Core / RIS

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Description Dissertation/Thesis