Showing papers on "Dimensionality reduction published in 1981"
TL;DR: This correspondence considers the extraction of features as a task of linear transformation of an initial pattern space into a new space, optimal with respect to discriminating the data.
Abstract: This correspondence considers the extraction of features as a task of linear transformation of an initial pattern space into a new space, optimal with respect to discriminating the data. A solution of the feature extraction problem is given for two multivariate normal distributed pattern classes using an extended Fisher criterion as the distance measure. The introduced distance measure consists of two terms. The first term estimates the distance between classes upon the difference of mean vectors of classes and the second one upon the difference of class covariance matrices. The proposed method is compared to some of the more popular alternative methods: Fukunaga-Koontz method and Foley-Sammon method.
84 citations
Journal Article•
TL;DR: An iterative procedure, the so-called power method, for finding a multivariate distribution's eigenvectors and eigenvalues is demonstrated and the projection of feature vectors onto the principal components is shown.
Abstract: The principal components transformation offers an effective methods for dimensionality reduction and for the assessment of the mutual dependence of observed variables in a data set. An iterative procedure, the so-called power method, for finding a multivariate distribution's eigenvectors and eigenvalues is demonstrated. The projection of feature vectors onto the principal components is shown.
14 citations
TL;DR: The triangulation method, recently proposed in the cluster analysis literature for mapping points from l-space to 2-space, is used to yield a simple and efficient algorithm for feature selection by interactive clustering.
3 citations
TL;DR: The PSM feature extraction technique is applied to a flaw characterization problem arising from ultrasonic nondestructive testing of materials and a deconvolution procedure is used to enhance pattern class discrimination.
Abstract: This paper focuses on extracting features from time series for pattern recognition. System identification techniques are used to represent the signals by a parameterized system model (PSM) with the parameter vector describing the PSM becoming the feature vector. A deconvolution procedure is used to enhance pattern class discrimination. The advantages of the PSM approach is a reduction of the dimensionality of the feature space thereby simplifying the classifier design and evaluation. The PSM feature extraction technique is applied to a flaw characterization problem arising from ultrasonic nondestructive testing of materials.
2 citations
24 Apr 1981
TL;DR: For a given textured image, experimental results on texture feature extraction, dimensionality reduction, and iterative Bayes classification are reported and the classification performance is superior to other texture discrimination methods examined on the same data.
Abstract: : For a given textured image, experimental results on texture feature extraction, dimensionality reduction, and iterative Bayes classification are reported The classification performance is superior to other texture discrimination methods that have been examined on the same data While the method presented is recommended as an effective statistical texture discrimination approach, the large amount of computation required, especially in feature extraction, can be undesirable in some applications Detailed computer program listing is given in the appendix
1 citations