Please use this identifier to cite or link to this item: http://scholarbank.nus.edu.sg/handle/10635/138222
Title: THEORETICAL ADVANCES IN CLUSTERING WITH APPLICATIONS TO MATRIX FACTORIZATION
Authors: LIU ZHAOQIANG
Keywords: clustering, k-means, mixture models, dimensionality reduction, error bounds, nonnegative matrix factorization
Issue Date: 25-Aug-2017
Source: LIU ZHAOQIANG (2017-08-25). THEORETICAL ADVANCES IN CLUSTERING WITH APPLICATIONS TO MATRIX FACTORIZATION. ScholarBank@NUS Repository.
Abstract: The main purpose of this thesis is to theoretically analyze the applications of clustering in various unsupervised learning problems, including the learning of mixture models and nonnegative matrix factorization (NMF). The thesis mainly consists of two parts. The first part considers the informativeness of the k-means algorithm, which is perhaps the most popular clustering algorithm, for learning mixture models. In the second part, we propose a geometric assumption on nonnegative data matrices such that under this assumption, we are able to provide upper bounds (both deterministic and probabilistic) on the relative error of nonnegative matrix factorization.
URI: http://scholarbank.nus.edu.sg/handle/10635/138222
Appears in Collections:Ph.D Theses (Open)

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