Papers of Guobiao Mei
Dimensionality Reduction Algorithms With Applications to Collaborative Data
and Images (2008)
by Guobiao Mei
Abstract:
General dimensionality reduction techniques play important roles in various
fields in machine learning. As a well studied problem, many existing algorithms
have achieved wide success in specific fields. In this work, we view this
problem from a different viewpoint.
We first focuses on collaborative data, which consist of ratings relating two
distinct sets of objects: users and items. Much of the work with such data
focuses on filtering: predicting unknown ratings for pairs of users and items.
In this work, we propose a well-structured Bayesian network to model the
collaborative data, and employ loopy belief propagation to estimate parameters
of the network and perform filtering tasks. In addition, we are interested in
the problem of visualizing the information in the collaborative data. Given all
of the ratings, our task is to embed all of the users and items as points in the
same Euclidean space. We would like to place users near items that they have
rated (or would rate) high, and far away from those they would give low ratings.
We pose this problem as a real-valued non-linear Bayesian network and employ
Markov chain Monte Carlo and expectation maximization to find an embedding. We
present a metric by which to judge the quality of a visualization.
We then extend the visualization framework to images, specifically to embed
images. Embedding images into a low dimensional space has a wide range of
applications: visualization, clustering, and preprocessing for supervised
learning. Traditional dimension reduction algorithms assume that the examples
densely populate the manifold. Image databases tend to break this assumption,
having isolated islands of similar images instead. Here we extend our framework
to achieve the embedding goal of preserving local image similarities based on
their scale invariant feature transform (SIFT) vectors. We make no neighborhood
assumptions in our embedding. Our algorithm can also embed the images in a
discrete grid, useful for many visualization tasks.
Download Information
Guobiao Mei (2008). "Dimensionality Reduction Algorithms With
Applications to Collaborative Data and Images." Doctoral
dissertation, University of California at Riverside. |
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Bibtex Citation
@phdthesis{Mei08,
author = "Guobiao Mei",
title = "Dimensionality Reduction Algorithms With Applications to Collaborative Data and Images",
school = "University of California at Riverside",
schoolabbr = "UC Riverside",
year = 2008,
month = Aug,
}
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