Showing papers on "Dimensionality reduction published in 1979"
TL;DR: This paper selectively surveys contributions in linear feature selection which have been developed for the analysis of multipass LANDSAT data in conjunction with the Large Area Crop Inventory Experiment.
21 citations
TL;DR: A model to reduce the dimension of the finite dimensional data is developed for the case when the covariance matrices are not necessarily equal and sufficient conditions are given so that the linear transformation of data of higher dimensions to lower dimensions does not increase the probabilities of misclassification.
10 citations
28 Dec 1979
TL;DR: This paper shows that a very fruitful area of application is in the area of dimensionality reduction in pattern recognition by preselecting cluster centers in the range and using regression techniques to derive the transformation.
Abstract: Linear regression is a powerful procedure with wide areas of application. We show in this paper that a very fruitful area of application is in the area of dimensionality reduction in pattern recognition. The dimensionality reduction is accomplished by preselecting cluster centers in the range and using regression techniques to derive the transformation. Experimental results are presented that compare this procedure to the Karhunen-Loeve procedure for several data sets.© (1979) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.