An empirical method to select dominant independent components in ICA for time series analysis

Yiu ming Cheung; Lei Xu

doi:10.1109/IJCNN.1999.830775

An empirical method to select dominant independent components in ICA for time series analysis

Yiu ming Cheung
, Lei Xu

Department of Computer Science

Research output: Chapter in book/report/conference proceeding › Conference proceeding › peer-review

21 Citations (Scopus)

Abstract

(Back and Weigend 1997) has shown that the dominant independent components obtained by independent component analysis (ICA) can reveal more underlying structure of the time series than principal component analysis. To find those dominant independent components, all the independent components are listed in an appropriate order and then a subset of components is selected according to the order. However, currently there does not exist a systematic way to choose such a subset. In this paper, we propose a number selection criterion to choose an appropriate dominant number, through which the dominant independent components can be automatically determined from a set of ordered components. Experiments on foreign exchange rates have shown the performance of this empirical method.

Original language	English
Title of host publication	IJCNN'99. International Joint Conference on Neural Networks. Proceedings
Publisher	IEEE
Pages	3883-3887
Number of pages	5
ISBN (Print)	0780355296
DOIs	https://doi.org/10.1109/IJCNN.1999.830775
Publication status	Published - 10 Jul 1999
Event	International Joint Conference on Neural Networks (IJCNN'99) - Washington, DC, USA Duration: 10 Jul 1999 → 16 Jul 1999

Publication series

Name	International Joint Conference on Neural Networks - Proceedings
ISSN (Print)	1098-7576

Conference

Conference	International Joint Conference on Neural Networks (IJCNN'99)
City	Washington, DC, USA
Period	10/07/99 → 16/07/99

Access to Document

10.1109/IJCNN.1999.830775

Cite this

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title = "An empirical method to select dominant independent components in ICA for time series analysis",

abstract = "(Back and Weigend 1997) has shown that the dominant independent components obtained by independent component analysis (ICA) can reveal more underlying structure of the time series than principal component analysis. To find those dominant independent components, all the independent components are listed in an appropriate order and then a subset of components is selected according to the order. However, currently there does not exist a systematic way to choose such a subset. In this paper, we propose a number selection criterion to choose an appropriate dominant number, through which the dominant independent components can be automatically determined from a set of ordered components. Experiments on foreign exchange rates have shown the performance of this empirical method.",

author = "Cheung, \{Yiu ming\} and Lei Xu",

note = "Publisher Copyright: {\textcopyright} 1999 IEEE; International Joint Conference on Neural Networks (IJCNN'99) ; Conference date: 10-07-1999 Through 16-07-1999",

year = "1999",

month = jul,

day = "10",

doi = "10.1109/IJCNN.1999.830775",

language = "English",

isbn = "0780355296",

series = "International Joint Conference on Neural Networks - Proceedings",

publisher = "IEEE",

pages = "3883--3887",

booktitle = "IJCNN'99. International Joint Conference on Neural Networks. Proceedings",

address = "United States",

}

Cheung, YM & Xu, L 1999, An empirical method to select dominant independent components in ICA for time series analysis. in IJCNN'99. International Joint Conference on Neural Networks. Proceedings. International Joint Conference on Neural Networks - Proceedings, IEEE, pp. 3883-3887, International Joint Conference on Neural Networks (IJCNN'99), Washington, DC, USA, 10/07/99. https://doi.org/10.1109/IJCNN.1999.830775

An empirical method to select dominant independent components in ICA for time series analysis. / Cheung, Yiu ming; Xu, Lei.
IJCNN'99. International Joint Conference on Neural Networks. Proceedings. IEEE, 1999. p. 3883-3887 (International Joint Conference on Neural Networks - Proceedings).

Research output: Chapter in book/report/conference proceeding › Conference proceeding › peer-review

TY - GEN

T1 - An empirical method to select dominant independent components in ICA for time series analysis

AU - Cheung, Yiu ming

AU - Xu, Lei

PY - 1999/7/10

Y1 - 1999/7/10

N2 - (Back and Weigend 1997) has shown that the dominant independent components obtained by independent component analysis (ICA) can reveal more underlying structure of the time series than principal component analysis. To find those dominant independent components, all the independent components are listed in an appropriate order and then a subset of components is selected according to the order. However, currently there does not exist a systematic way to choose such a subset. In this paper, we propose a number selection criterion to choose an appropriate dominant number, through which the dominant independent components can be automatically determined from a set of ordered components. Experiments on foreign exchange rates have shown the performance of this empirical method.

AB - (Back and Weigend 1997) has shown that the dominant independent components obtained by independent component analysis (ICA) can reveal more underlying structure of the time series than principal component analysis. To find those dominant independent components, all the independent components are listed in an appropriate order and then a subset of components is selected according to the order. However, currently there does not exist a systematic way to choose such a subset. In this paper, we propose a number selection criterion to choose an appropriate dominant number, through which the dominant independent components can be automatically determined from a set of ordered components. Experiments on foreign exchange rates have shown the performance of this empirical method.

UR - https://www.scopus.com/pages/publications/0033330927

U2 - 10.1109/IJCNN.1999.830775

DO - 10.1109/IJCNN.1999.830775

M3 - Conference proceeding

AN - SCOPUS:0033330927

SN - 0780355296

T3 - International Joint Conference on Neural Networks - Proceedings

SP - 3883

EP - 3887

BT - IJCNN'99. International Joint Conference on Neural Networks. Proceedings

PB - IEEE

T2 - International Joint Conference on Neural Networks (IJCNN'99)

Y2 - 10 July 1999 through 16 July 1999

ER -

An empirical method to select dominant independent components in ICA for time series analysis

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