東華大學圖書館 |

Language: English

Help

回圖書館首頁

手機版館藏查詢

Back

Switch To: Labeled | MARC Mode | ISBD

Kernel-based clustering and low rank...

Hong Kong University of Science and Technology (Hong Kong)., Computer Science.

Linked to FindBook

Google Book

Amazon

博客來

Kernel-based clustering and low rank approximation.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Kernel-based clustering and low rank approximation./
Author:	Zhang, Kai.
Description:	120 p.
Notes:	Source: Dissertation Abstracts International, Volume: 70-04, Section: B, page: 2402.
Contained By:	Dissertation Abstracts International70-04B.
Subject:	Computer Science. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3353519
ISBN:	9781109105834

Kernel-based clustering and low rank approximation.
Zhang, Kai.

Kernel-based clustering and low rank approximation. - 120 p.

Source: Dissertation Abstracts International, Volume: 70-04, Section: B, page: 2402.

Thesis (Ph.D.)--Hong Kong University of Science and Technology (Hong Kong), 2008.

Clustering is an unsupervised data exploration scenario that is of fundamental importance to pattern recognition and machine learning. This thesis involves two types of clustering paradigms, the mixture models and graph-based clustering methods, with the primary focus on how to improve the scaling behavior of related algorithms for large-scale application. With regard to mixture models, we are interested in reducing the model complexity in terms of number of components. We propose a unified algorithm to simultaneously solve "model simplification" and "component clustering", and apply it with success in a number of learning algorithms using mixture models, such as density based clustering and SVM testing. For graph-based clustering, we propose the density weighted Nystrom method for solving large scale eigenvalue problems, which demonstrates encouraging performance in the normalized-cut and kernel principal component analysis. We further extend this to the low rank approximation of kernel matrices, which is the key component to scaling up the kernel machines. We provide an error analysis on the Nystrom low rank approximation, based on which a new sampling scheme is proposed. Our scheme is very efficient and numerically outperforms a number of state-of-the-art approaches such as incomplete Cholesky decomposition, the standard Nystrom method, and probabilistic sampling approaches.

ISBN: 9781109105834Subjects--Topical Terms:

626642
Computer Science.

Kernel-based clustering and low rank approximation.
LDR:02234nmm 2200253 a 45 001 891488
005 20101111
008 101111s2008 ||||||||||||||||| ||eng d
020 $a 9781109105834
035 $a (UMI)AAI3353519
035 $a AAI3353519
040 $a UMI $c UMI
100 1 $a Zhang, Kai. $3 1034107
245 1 0 $a Kernel-based clustering and low rank approximation.
300 $a 120 p.
500 $a Source: Dissertation Abstracts International, Volume: 70-04, Section: B, page: 2402.
502 $a Thesis (Ph.D.)--Hong Kong University of Science and Technology (Hong Kong), 2008.
520 $a Clustering is an unsupervised data exploration scenario that is of fundamental importance to pattern recognition and machine learning. This thesis involves two types of clustering paradigms, the mixture models and graph-based clustering methods, with the primary focus on how to improve the scaling behavior of related algorithms for large-scale application. With regard to mixture models, we are interested in reducing the model complexity in terms of number of components. We propose a unified algorithm to simultaneously solve "model simplification" and "component clustering", and apply it with success in a number of learning algorithms using mixture models, such as density based clustering and SVM testing. For graph-based clustering, we propose the density weighted Nystrom method for solving large scale eigenvalue problems, which demonstrates encouraging performance in the normalized-cut and kernel principal component analysis. We further extend this to the low rank approximation of kernel matrices, which is the key component to scaling up the kernel machines. We provide an error analysis on the Nystrom low rank approximation, based on which a new sampling scheme is proposed. Our scheme is very efficient and numerically outperforms a number of state-of-the-art approaches such as incomplete Cholesky decomposition, the standard Nystrom method, and probabilistic sampling approaches.
590 $a School code: 1223.
650 4 $a Computer Science. $3 626642
690 $a 0984
710 2 $a Hong Kong University of Science and Technology (Hong Kong). $b Computer Science. $3 1065487
773 0 $t Dissertation Abstracts International $g 70-04B.
790 $a 1223
790 1 0 $a Kwok, James T. $e committee member
791 $a Ph.D.
792 $a 2008
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3353519