An, Yujia. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2005. / Includes bibliographical references (leaves 71-76). / Abstracts in English and Chinese. / Abstract --- p.ii / Acknowledgement --- p.iv / Chapter 1 --- Introduction --- p.1 / Chapter 1.1 --- Background --- p.1 / Chapter 1.1.1 --- Review on BFA --- p.2 / Chapter 1.1.2 --- Review on NFA --- p.3 / Chapter 1.1.3 --- Typical model selection criteria --- p.5 / Chapter 1.1.4 --- New model selection criterion and automatic model selection --- p.6 / Chapter 1.2 --- Our contributions --- p.7 / Chapter 1.3 --- Thesis outline --- p.8 / Chapter 2 --- Combination of B and BI architectures for BFA with automatic model selection --- p.10 / Chapter 2.1 --- Implementation of BFA using BYY harmony learning with au- tomatic model selection --- p.11 / Chapter 2.1.1 --- Basic issues of BFA --- p.11 / Chapter 2.1.2 --- B-architecture for BFA with automatic model selection . --- p.12 / Chapter 2.1.3 --- BI-architecture for BFA with automatic model selection . --- p.14 / Chapter 2.2 --- Local minima in B-architecture and BI-architecture --- p.16 / Chapter 2.2.1 --- Local minima in B-architecture --- p.16 / Chapter 2.2.2 --- One unstable result in BI-architecture --- p.21 / Chapter 2.3 --- Combination of B- and BI-architecture for BFA with automatic model selection --- p.23 / Chapter 2.3.1 --- Combine B-architecture and BI-architecture --- p.23 / Chapter 2.3.2 --- Limitations of BI-architecture --- p.24 / Chapter 2.4 --- Experiments --- p.25 / Chapter 2.4.1 --- Frequency of local minima occurring in B-architecture --- p.25 / Chapter 2.4.2 --- Performance comparison for several methods in B-architecture --- p.26 / Chapter 2.4.3 --- Comparison of local minima in B-architecture and BI- architecture --- p.26 / Chapter 2.4.4 --- Frequency of unstable cases occurring in BI-architecture --- p.27 / Chapter 2.4.5 --- Comparison of performance of three strategies --- p.27 / Chapter 2.4.6 --- Limitations of BI-architecture --- p.28 / Chapter 2.5 --- Summary --- p.29 / Chapter 3 --- A Comparative Investigation on Model Selection in Binary Factor Analysis --- p.31 / Chapter 3.1 --- Binary Factor Analysis and ML Learning --- p.32 / Chapter 3.2 --- Hidden Factors Number Determination --- p.33 / Chapter 3.2.1 --- Using Typical Model Selection Criteria --- p.33 / Chapter 3.2.2 --- Using BYY harmony Learning --- p.34 / Chapter 3.3 --- Empirical Comparative Studies --- p.36 / Chapter 3.3.1 --- Effects of Sample Size --- p.37 / Chapter 3.3.2 --- Effects of Data Dimension --- p.37 / Chapter 3.3.3 --- Effects of Noise Variance --- p.39 / Chapter 3.3.4 --- Effects of hidden factor number --- p.43 / Chapter 3.3.5 --- Computing Costs --- p.43 / Chapter 3.4 --- Summary --- p.46 / Chapter 4 --- A Comparative Investigation on Model Selection in Non-gaussian Factor Analysis --- p.47 / Chapter 4.1 --- Non-Gaussian Factor Analysis and ML Learning --- p.48 / Chapter 4.2 --- Hidden Factor Determination --- p.51 / Chapter 4.2.1 --- Using typical model selection criteria --- p.51 / Chapter 4.2.2 --- BYY harmony Learning --- p.52 / Chapter 4.3 --- Empirical Comparative Studies --- p.55 / Chapter 4.3.1 --- Effects of Sample Size on Model Selection Criteria --- p.56 / Chapter 4.3.2 --- Effects of Data Dimension on Model Selection Criteria --- p.60 / Chapter 4.3.3 --- Effects of Noise Variance on Model Selection Criteria --- p.64 / Chapter 4.3.4 --- Discussion on Computational Cost --- p.64 / Chapter 4.4 --- Summary --- p.68 / Chapter 5 --- Conclusions --- p.69 / Bibliography --- p.71
| Identifer | oai:union.ndltd.org:cuhk.edu.hk/oai:cuhk-dr:cuhk_325244 |
| Date | January 2005 |
| Contributors | An, Yujia., Chinese University of Hong Kong Graduate School. Division of Computer Science and Engineering. |
| Source Sets | The Chinese University of Hong Kong |
| Language | English, Chinese |
| Detected Language | English |
| Type | Text, bibliography |
| Format | print, xiv, 76 leaves : ill. ; 30 cm. |
| Rights | Use of this resource is governed by the terms and conditions of the Creative Commons “Attribution-NonCommercial-NoDerivatives 4.0 International” License (http://creativecommons.org/licenses/by-nc-nd/4.0/) |
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