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

Applications of kernel PCA methods t...

Tan, John.

Linked to FindBook

Google Book

Amazon

博客來

Applications of kernel PCA methods to geophysical data.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Applications of kernel PCA methods to geophysical data./
Author:	Tan, John.
Description:	207 p.
Notes:	Source: Dissertation Abstracts International, Volume: 66-11, Section: B, page: 6080.
Contained By:	Dissertation Abstracts International66-11B.
Subject:	Computer Science. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3194523
ISBN:	9780542393525

Applications of kernel PCA methods to geophysical data.
Tan, John.

Applications of kernel PCA methods to geophysical data. - 207 p.

Source: Dissertation Abstracts International, Volume: 66-11, Section: B, page: 6080.

Thesis (Ph.D.)--George Mason University, 2006.

Various scientific fields, particularly Earth system science require analysis of large and disparate data sets which describe a number of geophysical parameters. Quite often, one needs correlations between these parameters to identify underlying patterns. This work concentrates on the development and applications of some of these state-of-the-art techniques. Kernel Principal Component Analysis (KPCA) is an efficient generalization of traditional Principal Component Analysis (PCA) that allows for the detection and characterization of low-dimensional nonlinear structure in multivariate (high dimensional) data sets. We apply KPCA to two data sets: tropical Pacific sea surface temperature (SST) and Normalized Difference Vegetation Index (NDVI). The two data sets encompass sea and land respectively. The analysis exhibits correlations with ENSO activity in both data sets. Spatial anomaly patterns correlated to the ENSO are detected and in many cases match drought patterns more accurately than PCA. The impact of different kernel mappings is examined and the results are discussed. It is found that KPCA can provide results that have higher correlations with the representative ENSO time series and better resolution with the associated spatial patterns than its linear counterpart PCA. The complexity of the KPCA methodology introduced is on the same order of operations and memory requirements as standard PCA. Therefore it can be used in most areas where standard PCA is used, in order to better characterize inherent nonlinear structure in the data.

ISBN: 9780542393525Subjects--Topical Terms:

626642
Computer Science.

Applications of kernel PCA methods to geophysical data.
LDR:02500nmm 2200301 4500 001 1825397
005 20061211074619.5
008 130610s2006 eng d
020 $a 9780542393525
035 $a (UnM)AAI3194523
035 $a AAI3194523
040 $a UnM $c UnM
100 1 $a Tan, John. $3 617106
245 1 0 $a Applications of kernel PCA methods to geophysical data.
300 $a 207 p.
500 $a Source: Dissertation Abstracts International, Volume: 66-11, Section: B, page: 6080.
500 $a Directors: Menas Kafatos; Ruixan Yang.
502 $a Thesis (Ph.D.)--George Mason University, 2006.
520 $a Various scientific fields, particularly Earth system science require analysis of large and disparate data sets which describe a number of geophysical parameters. Quite often, one needs correlations between these parameters to identify underlying patterns. This work concentrates on the development and applications of some of these state-of-the-art techniques. Kernel Principal Component Analysis (KPCA) is an efficient generalization of traditional Principal Component Analysis (PCA) that allows for the detection and characterization of low-dimensional nonlinear structure in multivariate (high dimensional) data sets. We apply KPCA to two data sets: tropical Pacific sea surface temperature (SST) and Normalized Difference Vegetation Index (NDVI). The two data sets encompass sea and land respectively. The analysis exhibits correlations with ENSO activity in both data sets. Spatial anomaly patterns correlated to the ENSO are detected and in many cases match drought patterns more accurately than PCA. The impact of different kernel mappings is examined and the results are discussed. It is found that KPCA can provide results that have higher correlations with the representative ENSO time series and better resolution with the associated spatial patterns than its linear counterpart PCA. The complexity of the KPCA methodology introduced is on the same order of operations and memory requirements as standard PCA. Therefore it can be used in most areas where standard PCA is used, in order to better characterize inherent nonlinear structure in the data.
590 $a School code: 0883.
650 4 $a Computer Science. $3 626642
650 4 $a Statistics. $3 517247
650 4 $a Environmental Sciences. $3 676987
690 $a 0984
690 $a 0463
690 $a 0768
710 2 0 $a George Mason University. $3 1019450
773 0 $t Dissertation Abstracts International $g 66-11B.
790 1 0 $a Kafatos, Menas, $e advisor
790 1 0 $a Yang, Ruixan, $e advisor
790 $a 0883
791 $a Ph.D.
792 $a 2006
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3194523