A Problem of Dimensionality: A Simple Example

Authors:
G. V. Trunk
Affiliations:
Naval Research Laboratory, Washington, DC 20375.
Venue:
IEEE Transactions on Pattern Analysis and Machine Intelligence
Year:
1979

Citing 0
Cited 11

Original Contribution: Computer aided neural network engineering

Neural Networks
Random subspace method for multivariate feature selection

Pattern Recognition Letters
Feature selection in robust clustering based on Laplace mixture

Pattern Recognition Letters
Determining the saliency of feature measurements obtained from images of sedimentary organic matter for use in its classification

Computers & Geosciences
The peaking phenomenon in the presence of feature-selection

Pattern Recognition Letters
Performance of feature-selection methods in the classification of high-dimension data

Pattern Recognition
A neural network classifier with rough set-based feature selection to classify multiclass IC package products

Advanced Engineering Informatics
Efficient update of the covariance matrix inverse in iterated linear discriminant analysis

Pattern Recognition Letters
Feature selection with adjustable criteria

RSFDGrC'05 Proceedings of the 10th international conference on Rough Sets, Fuzzy Sets, Data Mining, and Granular Computing - Volume Part I
A finite mixture model for simultaneous high-dimensional clustering, localized feature selection and outlier rejection

Expert Systems with Applications: An International Journal
Is feature selection still necessary?

SLSFS'05 Proceedings of the 2005 international conference on Subspace, Latent Structure and Feature Selection

Quantified Score

Hi-index	0.15

Visualization

Abstract

In pattern recognition problems it has been noted that beyond a certain point the inclusion of additional parameters (that have been estimated) leads to higher probabilities of error. A simple problem has been formulated where the probability of error approaches zero as the dimensionality increases and all the parameters are known; on the other hand, the probability of error approaches one-half as the dimensionality increases and parameters are estimated.