Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/76715
Title: Learning sparse graphical models for data restoration and multi-label classification
Authors: Li, Qiang
Advisors: You, Jane (COMP)
Keywords: Data mining
Issue Date: 2018
Publisher: The Hong Kong Polytechnic University
Abstract: Sparse probabilistic graphical models play an important role in structured prediction when the dependency structure is unknown. By inducing sparsity over edge parameters, a typical sparse graphical model can combine structure learning and parameter estimation under a unified optimization framework. In this thesis, we propose three specific sparse graphical models accompanied by their applications in data restoration and multi-label classification respectively. For the data restoration task, we propose random mixed field (RMF) model to explore mixed-attribute correlations among data. The RMF model is capable of handling mixed-attribute data denoising and imputation simultaneously. Meanwhile, RMF employs a structured mean-field variational approach to decouple continuous-discrete interactions to achieve approximate inference. The effectiveness of this model is evaluated on both synthetic and real-world data. For the multi-label classification task, we propose correlated logistic model (CorrLog) and conditional graphical lasso (CGL), to learn conditional label correlations. (1) The CorrLog model characterizes pair-wise label correlations via scalar parameters, thus effects in an explicit (or direct) fashion. More specifically, CorrLog extends conventional logistic regression by jointly modelling label correlations. In addition, elastic-net regularization is employed to induce sparsity over the scalar parameters that define label correlations. CorrLog can be efficiently learned by regularized maximum pseudo likelihood estimation which enjoys a satisfying generalization bound. Besides, message passing algorithm is applied to solve the multi-label prediction problem. (2) The CGL model further leverages features in modelling pairwise label correlations in terms of parametric functions of the input features, which effects in an implicit (or indirect) fashion. In general, CGL provides a unified Bayesian framework for structure and parameter learning conditioned on input features. We formulate the multi-label prediction as CGL inference problem, which is solved by a mean field variational approach. Meanwhile, CGL learning is efficient after applying the maximum a posterior (MAP) methodology and solved by a proximal gradient procedure. The effectiveness of CorrLog and CGL are evaluated on several benchmark multi-label classification datasets.
Description: xviii, 127 pages : color illustrations
PolyU Library Call No.: [THS] LG51 .H577P COMP 2018 LiQ
URI: http://hdl.handle.net/10397/76715
Rights: All rights reserved.
Appears in Collections:Thesis

Files in This Item:
File Description SizeFormat 
991022096434003411_link.htmFor PolyU Users167 BHTMLView/Open
991022096434003411_pira.pdfFor All Users (Non-printable)1.63 MBAdobe PDFView/Open
Show full item record
PIRA download icon_1.1View/Download Contents

Page view(s)

5
Citations as of Jul 10, 2018

Download(s)

2
Citations as of Jul 10, 2018

Google ScholarTM

Check


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.