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Linear feature selection with applicationsSeveral ways in which feature selection techniques were used in LACIE are discussed. In all cases, the methods require some a priori information and assumptions; in most, the classification procedure (Bayes optimal) was chosen in advance. The transformations used for dimensionality reduction are linear, that is, the variables in feature space are always linear combinations of the original measurements. Several numerically tractable criteria developed for LACIE, which provide information about the probability of misclassification, are discussed. Recent results on linear feature selection techniques are included. Their use in LACIE is discussed. Related open questions are mentioned.
Document ID
19800007236
Acquisition Source
Legacy CDMS
Document Type
Conference Paper
Authors
Decell, H. P., Jr.
(Houston Univ. TX, United States)
Guseman, L. F., Jr.
(Texas A and M Univ. College Station, United States)
Date Acquired
August 10, 2013
Publication Date
July 1, 1979
Publication Information
Publication: NASA. Johnson Space Center Proc. of Tech. Sessions, Vol. 1 and 2
Subject Category
Earth Resources And Remote Sensing
Accession Number
80N15496
Funding Number(s)
CONTRACT_GRANT: NAS9-15000
CONTRACT_GRANT: NAS9-14689
Distribution Limits
Public
Copyright
Work of the US Gov. Public Use Permitted.

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