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Large-Scale Machine Learning for Classification and Search (2012)
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BibTeX
@MISC{Liu12large-scalemachine,
author = {Wei Liu},
title = {Large-Scale Machine Learning for Classification and Search},
year = {2012}
}
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Abstract
With the rapid development of the Internet, nowadays tremendous amounts of data including images and videos, up to millions or billions, can be collected for training machine learning models. Inspired by this trend, this thesis is dedicated to developing large-scale machine learning techniques for the purpose of making classification and nearest neighbor search practical on gigantic databases. Our first approach is to explore data graphs to aid classification and nearest neighbor search. A graph offers an attractive way of representing data and discovering the essential information such as the neighborhood structure. However, both of the graph construction process and graph-based learning techniques become computationally prohibitive at a large scale. To this end, we present an efficient large graph construction approach and subsequently apply it to develop scalable semi-supervised learning and unsupervised hashing algorithms. Our unique contributions on the graph-related topics include: 1. Large Graph Construction: Conventional neighborhood graphs such as kNN graphs require a quadratic time complexity, which is inadequate for large-scale applications mentioned above. To overcome this bottleneck, we present a novel graph construction approach,
Keyphrases
large-scale machine learning novel graph construction approach neighborhood structure machine learning model unique contribution tremendous amount rapid development neighbor search graph-based learning technique large graph construction unsupervised hashing algorithm attractive way efficient large graph construction approach first approach large-scale application graph construction process essential information graph-related topic quadratic time complexity scalable semi-supervised learning large scale neighbor search practical gigantic database large-scale machine conventional neighborhood graph data graph
