<abstract xmlns="http://www.w3.org/1999/xhtml"><p> The current paper presents a method to deliver non- linear projections of a data set that discriminate between existing labeled groups of data items. Inspired from traditional linear Projection Pursuit and Linear Discriminant Analysis, the new method seeks nonlinear combinations of attributes as polynomials that maximize Fisher’s criterion. The search for the monomials in a polynomial is conducted in a logarithmic space in order to reduce computational complexity. The selection of monomials and the optimization of weights that conduct to the nonlinear projection are performed with a multi-modal Genetic Algorithm hybridized with Differential Evolution. By alleviating the drawbacks driven from the linearity assumptions in traditional Projection Pursuit, the new method could gain a wide applicability in both unsupervised and supervised data analysis.</p></abstract>

The current paper presents a method to deliver non- linear projections of a data set that discriminate between existing labeled groups of data items. Inspired from traditional linear Projection Pursuit and Linear Discriminant Analysis, the new method seeks nonlinear combinations of attributes as polynomials that maximize Fisher’s criterion. The search for the monomials in a polynomial is conducted in a logarithmic space in order to reduce computational complexity. The selection of monomials and the optimization of weights that conduct to the nonlinear projection are performed with a multi-modal Genetic Algorithm hybridized with Differential Evolution. By alleviating the drawbacks driven from the linearity assumptions in traditional Projection Pursuit, the new method could gain a wide applicability in both unsupervised and supervised data analysis.

Nonlinear Feature Extraction in a Logarithmic Space with Evolutionary Algorithms

Faculty of Computer Science Al. I. Cuza University of Iasi

Department of Computer Science University of Massachusetts Boston

Annals of West University of Timisoara - Mathematics and Computer Science

{"article-title":"Nonlinear Feature Extraction in a Logarithmic Space with Evolutionary Algorithms"}

The current paper presents a method to deliver non- linear projections of a data set that discriminate between existing labeled groups of data items. Inspired...

Nonlinear Feature Extraction in a Logarithmic Space with Evolutionary Algorithms

Online veröffentlicht: 14. Aug. 2013

Seitenbereich: 25 - 36

DOI: https://doi.org/10.2478/awutm-2013-0002

Schlüsselwörter
nonlinear feature extraction, evolutionary algo- rithms, linear discriminant analysis

This content is open access.

Nonlinear Feature Extraction in a Logarithmic Space with Evolutionary Algorithms

Online veröffentlicht: 14. Aug. 2013

Seitenbereich: 25 - 36

DOI: https://doi.org/10.2478/awutm-2013-0002

Schlüsselwörternonlinear feature extraction, evolutionary algo- rithms, linear discriminant analysis

This content is open access.

Schlüsselwörter
nonlinear feature extraction, evolutionary algo- rithms, linear discriminant analysis