Analyzing high dimensional data

Chulhee Lee, David A. Landgrebe

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (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 languageEnglish
Title of host publicationIGARSS 1992 - International Geoscience and Remote Sensing Symposium
Subtitle of host publicationInternational Space Year: Space Remote Sensing
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages561-563
Number of pages3
Volume1
ISBN (Electronic)0780301382
DOIs
Publication statusPublished - 1992 Jan 1
Event12th Annual International Geoscience and Remote Sensing Symposium, IGARSS 1992 - Houston, United States
Duration: 1992 May 261992 May 29

Other

Other12th Annual International Geoscience and Remote Sensing Symposium, IGARSS 1992
CountryUnited States
CityHouston
Period92/5/2692/5/29

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Statistics
Color codes
statistics
Classifiers
code

All Science Journal Classification (ASJC) codes

  • Computer Science Applications
  • Earth and Planetary Sciences(all)

Cite this

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

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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PY - 1992/1/1

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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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Lee C, Landgrebe DA. Analyzing high dimensional data. In IGARSS 1992 - International Geoscience and Remote Sensing Symposium: International Space Year: Space Remote Sensing. Vol. 1. Institute of Electrical and Electronics Engineers Inc. 1992. p. 561-563. 576770 https://doi.org/10.1109/IGARSS.1992.576770