Analyzing high dimensional data

Chulhee Lee; David A. Landgrebe

doi:10.1109/IGARSS.1992.576770

Analyzing high dimensional data

Chulhee Lee, David A. Landgrebe

Department of Electrical and Electronic Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

4 Citations (Scopus)

Abstract

In this paper, we discuss problems encountered in analyzing high dimensional data and propose possible solutions. We first recognize the increased importance of the second order statistics in analyzing high dimensional data and the shortcoming of the minimum distance classifier in high dimensional data. By investigating characteristics of high dimensional data, we suggest the reson why the second order statistics must be taken into account in high dimensional data. Recognizing the importance of the second order statistics, there is a need to represent the second order statistics effectively. However, as the data dimensionality increases, it becomes more difficult to perceive and compare information present in statistics derived from data. In order to overcome such a problem, we propose a method to visualize statistics using color code. By representing statistics using a color code, one can more easily compare the first and the second statistics.

Original language	English
Title of host publication	IGARSS 1992 - International Geoscience and Remote Sensing Symposium
Subtitle of host publication	International Space Year: Space Remote Sensing
Editors	Ruby Williamson, Tammy Stein
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	561-563
Number of pages	3
ISBN (Electronic)	0780301382
DOIs	https://doi.org/10.1109/IGARSS.1992.576770
Publication status	Published - 1992
Event	12th Annual International Geoscience and Remote Sensing Symposium, IGARSS 1992 - Houston, United States Duration: 1992 May 26 → 1992 May 29

Publication series

Name	International Geoscience and Remote Sensing Symposium (IGARSS)
Volume	1

Other

Other	12th Annual International Geoscience and Remote Sensing Symposium, IGARSS 1992
Country/Territory	United States
City	Houston
Period	92/5/26 → 92/5/29

Bibliographical note

Funding Information:
This work was funded in part by NASA under grant NAGW-925.

All Science Journal Classification (ASJC) codes

Computer Science Applications
Earth and Planetary Sciences(all)

Access to Document

10.1109/IGARSS.1992.576770

Cite this

Lee, C., & Landgrebe, D. A. (1992). Analyzing high dimensional data. In R. Williamson, & T. Stein (Eds.), IGARSS 1992 - International Geoscience and Remote Sensing Symposium: International Space Year: Space Remote Sensing (pp. 561-563). [576770] (International Geoscience and Remote Sensing Symposium (IGARSS); Vol. 1). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/IGARSS.1992.576770

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title = "Analyzing high dimensional data",

abstract = "In this paper, we discuss problems encountered in analyzing high dimensional data and propose possible solutions. We first recognize the increased importance of the second order statistics in analyzing high dimensional data and the shortcoming of the minimum distance classifier in high dimensional data. By investigating characteristics of high dimensional data, we suggest the reson why the second order statistics must be taken into account in high dimensional data. Recognizing the importance of the second order statistics, there is a need to represent the second order statistics effectively. However, as the data dimensionality increases, it becomes more difficult to perceive and compare information present in statistics derived from data. In order to overcome such a problem, we propose a method to visualize statistics using color code. By representing statistics using a color code, one can more easily compare the first and the second statistics.",

author = "Chulhee Lee and Landgrebe, {David A.}",

note = "Funding Information: This work was funded in part by NASA under grant NAGW-925.; 12th Annual International Geoscience and Remote Sensing Symposium, IGARSS 1992 ; Conference date: 26-05-1992 Through 29-05-1992",

year = "1992",

doi = "10.1109/IGARSS.1992.576770",

language = "English",

series = "International Geoscience and Remote Sensing Symposium (IGARSS)",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

pages = "561--563",

editor = "Ruby Williamson and Tammy Stein",

booktitle = "IGARSS 1992 - International Geoscience and Remote Sensing Symposium",

address = "United States",

}

Lee, C & Landgrebe, DA 1992, Analyzing high dimensional data. in R Williamson & T Stein (eds), IGARSS 1992 - International Geoscience and Remote Sensing Symposium: International Space Year: Space Remote Sensing., 576770, International Geoscience and Remote Sensing Symposium (IGARSS), vol. 1, Institute of Electrical and Electronics Engineers Inc., pp. 561-563, 12th Annual International Geoscience and Remote Sensing Symposium, IGARSS 1992, Houston, United States, 92/5/26. https://doi.org/10.1109/IGARSS.1992.576770

Analyzing high dimensional data. / Lee, Chulhee; Landgrebe, David A.

IGARSS 1992 - International Geoscience and Remote Sensing Symposium: International Space Year: Space Remote Sensing. ed. / Ruby Williamson; Tammy Stein. Institute of Electrical and Electronics Engineers Inc., 1992. p. 561-563 576770 (International Geoscience and Remote Sensing Symposium (IGARSS); Vol. 1).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

TY - GEN

T1 - Analyzing high dimensional data

AU - Lee, Chulhee

AU - Landgrebe, David A.

N1 - Funding Information: This work was funded in part by NASA under grant NAGW-925.

PY - 1992

Y1 - 1992

N2 - In this paper, we discuss problems encountered in analyzing high dimensional data and propose possible solutions. We first recognize the increased importance of the second order statistics in analyzing high dimensional data and the shortcoming of the minimum distance classifier in high dimensional data. By investigating characteristics of high dimensional data, we suggest the reson why the second order statistics must be taken into account in high dimensional data. Recognizing the importance of the second order statistics, there is a need to represent the second order statistics effectively. However, as the data dimensionality increases, it becomes more difficult to perceive and compare information present in statistics derived from data. In order to overcome such a problem, we propose a method to visualize statistics using color code. By representing statistics using a color code, one can more easily compare the first and the second statistics.

AB - In this paper, we discuss problems encountered in analyzing high dimensional data and propose possible solutions. We first recognize the increased importance of the second order statistics in analyzing high dimensional data and the shortcoming of the minimum distance classifier in high dimensional data. By investigating characteristics of high dimensional data, we suggest the reson why the second order statistics must be taken into account in high dimensional data. Recognizing the importance of the second order statistics, there is a need to represent the second order statistics effectively. However, as the data dimensionality increases, it becomes more difficult to perceive and compare information present in statistics derived from data. In order to overcome such a problem, we propose a method to visualize statistics using color code. By representing statistics using a color code, one can more easily compare the first and the second statistics.

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M3 - Conference contribution

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T3 - International Geoscience and Remote Sensing Symposium (IGARSS)

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BT - IGARSS 1992 - International Geoscience and Remote Sensing Symposium

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Lee C, Landgrebe DA. Analyzing high dimensional data. In Williamson R, Stein T, editors, IGARSS 1992 - International Geoscience and Remote Sensing Symposium: International Space Year: Space Remote Sensing. Institute of Electrical and Electronics Engineers Inc. 1992. p. 561-563. 576770. (International Geoscience and Remote Sensing Symposium (IGARSS)). doi: 10.1109/IGARSS.1992.576770

Analyzing high dimensional data

Abstract

Publication series

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Bibliographical note

All Science Journal Classification (ASJC) codes

Access to Document

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Cite this