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Author Bozorgmehr, Pouya
Title An efficient online feature extraction algorithm for neural networks
book jacket
Descript 76 p
Note Source: Masters Abstracts International, Volume: 48-02, page: 1076
Adviser: Gary Cottrell
Thesis (M.S.)--University of California, San Diego, 2009
Finding optimal feature sets for classification tasks is still a fundamental and challenging problem in the area of machine learning. The human visual system performs classification tasks effortlessly using its hierarchical features and efficient coding in its visual pathway. It is shown that early in the visual system the information is encoded using distributed coding schemes and later in the visual system the sparse coding is utilized. We propose a biologically motivated method to extract features that encode the information according to a specific activation profile. We show how our model much like the visual system, can learn distributed coding in lower layers and sparse coding in higher layers in an online manner. Online feature extraction is used in biometrics, machine vision, and pattern recognition. Methods that can dynamically extract features and perform online classification are especially important for real-world applications. We introduce online algorithms that are fast and efficient in extracting features for encoding and discriminating the input space. We also show a supervised version of this algorithm that performs feature selection and extraction in alternating steps to achieve a fast convergence and high accuracy
School code: 0033
Host Item Masters Abstracts International 48-02
Subject Artificial Intelligence
0800
Alt Author University of California, San Diego. Computer Science and Engineering
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