An Efficient Optimization Method for Revealing Local Optima of Projection Pursuit Indices

Marie-Sainte, Souad Larabi; Berro, Alain; Ruiz‐Gazen, Anne

doi:10.1007/978-3-642-15461-4_6

Cited by 9 publications

(3 citation statements)

References 10 publications

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“…We also consider a new proposal suited to the detection of clusters called ''discriminant index" . All these indices are well defined and applied for the detection of clusters and/or outliers in Berro et al (2010) and Mari-Sainte et al (2010).…”

Section: Projection Pursuitmentioning

confidence: 99%

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Detection and visualization of non-linear structures in large datasets using Exploratory Projection Pursuit Laboratory (EPP-Lab) software

Marie-Sainte

2017

Journal of King Saud University - Computer and Information Scie

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This article consists of using biologically inspired algorithms in order to detect potentially interesting structures in large and multidimensional data sets. Data exploration and the detection of interesting structures are based on the use of Projection Pursuit that involves the definition and the optimization of an index associated with each direction or projection. The optimization of a projection index should provide a set of multiple optima that is expected to correspond to interesting graphical representations in low dimensional space. The implementation of the bio-inspired algorithms along with the projection pursuit develops a new software called EPP-Lab. Projection pursuit is widely used in different scientific domains (biology, pharmacy, bioinformatics, biometry, etc) but not widely present in the well-known softwares. EPP-Lab is dedicated to recognize and visualize clusters and outlying observations on one dimension from high dimensional and multivariate data sets. It includes different statistical techniques for results analysis. It provides several features and gives the user the option to adjust the parameters of the selected bio-inspired methods or to use defaults values. EPP-Lab is a unique software for detection, visualization and analysis of non-linear structures. The performance of this tool has been validated by testing different real and simulated data sets. Ó 2016 The Author. Production and hosting by Elsevier B.V. on behalf of King Saud University. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

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Section: Projection Pursuitmentioning

confidence: 99%

“…Among the different bio-inspired algorithms, Genetic Algorithm (GA), Particle Swarm Optimization (PSO) and a hybrid Particle Swarm Optimization method called Tribes are employed. The performance of these selected methods combined with PP has been validated in Berro et al (2010) and Mari-Sainte et al (2010).…”

Section: Introductionmentioning

confidence: 99%

Detection and visualization of non-linear structures in large datasets using Exploratory Projection Pursuit Laboratory (EPP-Lab) software

Marie-Sainte

2017

Journal of King Saud University - Computer and Information Scie

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“…Early approaches in this respect were based on the gradient techniques [30,29] and Newton-Raphson [31,37,14,13], where the projections are performed in at most three dimensions for visual exploratory tasks, the so-called exploratory projection pursuit (EPP). Further developments focused on developing more global methods for PP optimization, such as random search [38,39,29], genetic algorithm (GA) [32], random scan sampling algorithm (RSSA) [34], simulated annealing (SA) [21], particle swarm optimization (PSO) [35] and tribes [40]. In a previous work [33] we describe PPGA, a GA optimizer with a specialized crossover operator that often showed to find solutions better than those found by PSO, RSSA, and SA when used inside the SPP framework, reason why it is adopted for the present work.…”

Section: Pp Optimizationmentioning

confidence: 99%

A Projection Pursuit framework for supervised dimension reduction of high dimensional small sample datasets

et al. 2015

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Multiobjective Projection Pursuit for Semisupervised Feature Extraction

Breaban

2013

Applications of Evolutionary Computation

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An Efficient Optimization Method for Revealing Local Optima of Projection Pursuit Indices

Cited by 9 publications

References 10 publications

Detection and visualization of non-linear structures in large datasets using Exploratory Projection Pursuit Laboratory (EPP-Lab) software

Detection and visualization of non-linear structures in large datasets using Exploratory Projection Pursuit Laboratory (EPP-Lab) software

A Projection Pursuit framework for supervised dimension reduction of high dimensional small sample datasets

Multiobjective Projection Pursuit for Semisupervised Feature Extraction

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