Statistical machine learning for data mining and collaborative multimedia retrieval.
Another issue studied in the framework is Distance Metric Learning (DML). Learning distance metrics is critical to many machine learning tasks, especially when contextual information is available. To learn effective metrics from pairwise contextual constraints, two novel methods, Discriminative Comp...
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ftchinunihkuls:oai:cuhk-dr:cuhk_343930 2023-05-15T16:02:06+02:00 Statistical machine learning for data mining and collaborative multimedia retrieval. CUHK electronic theses & dissertations collection Hoi, Chu Hong. Chinese University of Hong Kong Graduate School. Division of Computer Science and Engineering. 2006 electronic resource microform microfiche 1 online resource (xviii, 181 p. 223: ill.) http://library.cuhk.edu.hk/record=b6074301 https://repository.lib.cuhk.edu.hk/en/item/cuhk-343930 eng chi eng chi cuhk:343930 http://library.cuhk.edu.hk/record=b6074301 https://repository.lib.cuhk.edu.hk/en/item/cuhk-343930 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/) Data mining Information storage and retrieval systems Machine learning--Statistical methods Multimedia systems Text theses 2006 ftchinunihkuls 2023-03-10T02:09:49Z Another issue studied in the framework is Distance Metric Learning (DML). Learning distance metrics is critical to many machine learning tasks, especially when contextual information is available. To learn effective metrics from pairwise contextual constraints, two novel methods, Discriminative Component Analysis (DCA) and Kernel DCA, are proposed to learn both linear and nonlinear distance metrics. Empirical results on data clustering validate the advantages of the algorithms. Based on this unified learning framework, a novel scheme is suggested for learning Unified Kernel Machines (UKM). The UKM scheme combines supervised kernel machine learning, unsupervised kernel de sign, semi-supervised kernel learning, and active learning in an effective fashion. A key component in the UKM scheme is to learn kernels from both labeled and unlabeled data. To this purpose; a new Spectral Kernel Learning (SKL) algorithm is proposed, which is related to a quadratic program. Empirical results show that the UKM technique is promising for classification tasks. In addition to the above methodologies, this thesis also addresses some practical issues in applying machine learning techniques to real-world applications. For example, in a time-dependent data mining application, in order to design a domain-specific kernel, marginalized kernel techniques are suggested to formulate an effective kernel aimed at web data mining tasks. Last, the thesis investigates statistical machine learning techniques with applications to multimedia retrieval and addresses some practical issues, such as robustness to noise and scalability. To bridge semantic gap issues of multimedia retrieval, a Collaborative Multimedia Retrieval (CMR) scheme is proposed to exploit historical log data of users' relevance feedback for improving retrieval tasks. Two types of learning tasks in the CMR scheme are identified and two innovative algorithms are proposed to effectively solve the problems respectively. Statistical machine learning techniques have been widely ... Thesis DML The Chinese University of Hong Kong: CUHK Digital Repository |
institution |
Open Polar |
collection |
The Chinese University of Hong Kong: CUHK Digital Repository |
op_collection_id |
ftchinunihkuls |
language |
English Chinese |
topic |
Data mining Information storage and retrieval systems Machine learning--Statistical methods Multimedia systems |
spellingShingle |
Data mining Information storage and retrieval systems Machine learning--Statistical methods Multimedia systems Statistical machine learning for data mining and collaborative multimedia retrieval. |
topic_facet |
Data mining Information storage and retrieval systems Machine learning--Statistical methods Multimedia systems |
description |
Another issue studied in the framework is Distance Metric Learning (DML). Learning distance metrics is critical to many machine learning tasks, especially when contextual information is available. To learn effective metrics from pairwise contextual constraints, two novel methods, Discriminative Component Analysis (DCA) and Kernel DCA, are proposed to learn both linear and nonlinear distance metrics. Empirical results on data clustering validate the advantages of the algorithms. Based on this unified learning framework, a novel scheme is suggested for learning Unified Kernel Machines (UKM). The UKM scheme combines supervised kernel machine learning, unsupervised kernel de sign, semi-supervised kernel learning, and active learning in an effective fashion. A key component in the UKM scheme is to learn kernels from both labeled and unlabeled data. To this purpose; a new Spectral Kernel Learning (SKL) algorithm is proposed, which is related to a quadratic program. Empirical results show that the UKM technique is promising for classification tasks. In addition to the above methodologies, this thesis also addresses some practical issues in applying machine learning techniques to real-world applications. For example, in a time-dependent data mining application, in order to design a domain-specific kernel, marginalized kernel techniques are suggested to formulate an effective kernel aimed at web data mining tasks. Last, the thesis investigates statistical machine learning techniques with applications to multimedia retrieval and addresses some practical issues, such as robustness to noise and scalability. To bridge semantic gap issues of multimedia retrieval, a Collaborative Multimedia Retrieval (CMR) scheme is proposed to exploit historical log data of users' relevance feedback for improving retrieval tasks. Two types of learning tasks in the CMR scheme are identified and two innovative algorithms are proposed to effectively solve the problems respectively. Statistical machine learning techniques have been widely ... |
author2 |
Hoi, Chu Hong. Chinese University of Hong Kong Graduate School. Division of Computer Science and Engineering. |
format |
Thesis |
title |
Statistical machine learning for data mining and collaborative multimedia retrieval. |
title_short |
Statistical machine learning for data mining and collaborative multimedia retrieval. |
title_full |
Statistical machine learning for data mining and collaborative multimedia retrieval. |
title_fullStr |
Statistical machine learning for data mining and collaborative multimedia retrieval. |
title_full_unstemmed |
Statistical machine learning for data mining and collaborative multimedia retrieval. |
title_sort |
statistical machine learning for data mining and collaborative multimedia retrieval. |
publishDate |
2006 |
url |
http://library.cuhk.edu.hk/record=b6074301 https://repository.lib.cuhk.edu.hk/en/item/cuhk-343930 |
genre |
DML |
genre_facet |
DML |
op_relation |
cuhk:343930 http://library.cuhk.edu.hk/record=b6074301 https://repository.lib.cuhk.edu.hk/en/item/cuhk-343930 |
op_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/) |
_version_ |
1766397720888606720 |