Access Type

Open Access Thesis

Date of Award

January 2015

Degree Type

Thesis

Degree Name

M.S.

Department

Computer Science

First Advisor

Xue-wen Chen

Abstract

High dimensionality and the sheer size of unlabeled data available today demand

new development in unsupervised learning of sparse representation. Despite of recent

advances in representation learning, most of the current methods are limited when

dealing with large scale unlabeled data. In this study, we propose a new unsupervised

method that is able to learn sparse representation from unlabeled data efficiently. We

derive a closed-form solution based on the sequential minimal optimization (SMO)

for training an auto encoder-decoder module, which efficiently extracts sparse and

compact features from any data set with various size. The inference process in the

proposed learning algorithm does not require any expensive Hessian computation

for solving the underlying optimization problems. Decomposition of the non-convex

optimization problem in our model enables us to solve each sub-problems analytically.

Using several image datasets including CIFAR-10, CALTECH-101 and AR

face database, we demonstrate the effectiveness in terms of computation time and

classification accuracy. Proposed method discovers dictionaries that are able to capture

low level features in larger dimensional patches in quite lower executional time

than the other alternatives. Then by detailed experimental results, we present that

our module outperforms various similar single layer state-of-the-art methods including

Sparse Filtering and K-Means clustering method.

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