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Title: Learning with Sparsity and Scattering Networks
Authors: Chen, Xu
Advisors: Ramadge, Peter
Contributors: Electrical Engineering Department
Subjects: Computer science
Issue Date: 2015
Publisher: Princeton, NJ : Princeton University
Abstract: The past decades have witnessed dramatic progress in artificial intelligence. Machine learning technology now powers many aspects of modern life, but the challenges remain. New learning algorithms and architectures need to be developed to take advantage of the increases in the amount of available computation and data. This thesis focuses on two of the most important guiding principles regarding learning algorithms, sparsity and depth. Sparsity leads to efficient and compact representations. It is a key element in domains including statistics, signal processing and machine learning. Depth allows discovering intricate structure and learning high-level abstraction. It is the reason for many recent breakthroughs in processing images, video, speech, audio and texts. By designing learning and classification algorithms that combine sparsity and depth, we show that these two concepts benefit each other and bring about improvements of classification performance. We first show the effectiveness of combining sparse representation-based classification with a special type of deep convolutional networks called scattering, in the context of music genre classication. Then we propose an unsupervised deep learning model with sparsity criteria for the classication of high-dimensional unstructured data. In both cases, variants of strategies and architectures are presented, and state-of-the-art results are obtained.
Alternate format: The Mudd Manuscript Library retains one bound copy of each dissertation. Search for these copies in the library's main catalog:
Type of Material: Academic dissertations (Ph.D.)
Language: en
Appears in Collections:Electrical Engineering

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