All a b c d e f g h i j k l m n o p q r s t u v w x y z Other

Page 1

Displaying 1 – 14 of 14

Showing per page

Data mining methods for gene selection on the basis of gene expression arrays

Michał Muszyński, Stanisław Osowski (2014)

International Journal of Applied Mathematics and Computer Science

The paper presents data mining methods applied to gene selection for recognition of a particular type of prostate cancer on the basis of gene expression arrays. Several chosen methods of gene selection, including the Fisher method, correlation of gene with a class, application of the support vector machine and statistical hypotheses, are compared on the basis of clustering measures. The results of applying these individual selection methods are combined together to identify the most often selected...

Data-driven penalty calibration: A case study for gaussian mixture model selection

Cathy Maugis, Bertrand Michel (2011)

ESAIM: Probability and Statistics

In the companion paper [C. Maugis and B. Michel, A non asymptotic penalized criterion for Gaussian mixture model selection. ESAIM: P&S 15 (2011) 41–68] , a penalized likelihood criterion is proposed to select a Gaussian mixture model among a specific model collection. This criterion depends on unknown constants which have to be calibrated in practical situations. A “slope heuristics” method is described and experimented to deal with this practical problem. In a model-based clustering context,...

Data-driven penalty calibration: A case study for Gaussian mixture model selection

Cathy Maugis, Bertrand Michel (2012)

ESAIM: Probability and Statistics

In the companion paper [C. Maugis and B. Michel, A non asymptotic penalized criterion for Gaussian mixture model selection. ESAIM: P&S15 (2011) 41–68] , a penalized likelihood criterion is proposed to select a Gaussian mixture model among a specific model collection. This criterion depends on unknown constants which have to be calibrated in practical situations. A “slope heuristics” method is described and experimented to deal with this practical problem. In a model-based clustering context, the...

Denoising Manifolds for Dimension

Jammalamadaka, Arvind K. (2009)

Serdica Mathematical Journal

2000 Mathematics Subject Classification: 68T01, 62H30, 32C09.Locally Linear Embedding (LLE) has gained prominence as a tool in unsupervised non-linear dimensional reduction. While the algorithm aims to preserve certain proximity relations between the observed points, this may not always be desirable if the shape in higher dimensions that we are trying to capture is observed with noise. This note suggests that a desirable first step is to remove or at least reduce the noise in the observations before...

Detecting a data set structure through the use of nonlinear projections search and optimization

Victor L. Brailovsky, Michael Har-Even (1998)

Kybernetika

Detecting a cluster structure is considered. This means solving either the problem of discovering a natural decomposition of data points into groups (clusters) or the problem of detecting clouds of data points of a specific form. In this paper both these problems are considered. To discover a cluster structure of a specific arrangement or a cloud of data of a specific form a class of nonlinear projections is introduced. Fitness functions that estimate to what extent a given subset of data points...

Dissimilarites de type spherique et positionnement multidimensionnel normé

Farid Beninel (2010)

RAIRO - Operations Research

Our concern here, is the characterization of dissimilarity indexes defined over finite sets, whose spatial representation is spherical. Consequently, we propose a methodology (Normed MultiDimensional Scaling) to determine the spherical euclidean representation of a set of items best accounting for the initial dissimilarity between items. This methodology has the advantage of being graphically readable on individual qualities of projection like the normed PCA, of which it constitutes a generalization....

Dissimilarités multivoies et généralisations d'hypergraphes sans triangles

Jean Diatta (1997)

Mathématiques et Sciences Humaines

Les dissimilarités multivoies sont une généralisation naturelle des dissimilarités usuelles deux voies. Dans ce papier, des classes de dissimilarités multivoies sont étudiées, ainsi que des modèles de passage d'un nombre de voies donné à un autre nombre de voies. Une application à la spécification de systèmes classifiants a conduit à une bijection entre une classe de dissimilarités multivoies et une famille de systèmes stratifiés de classifccation.

Currently displaying 1 – 14 of 14

Page 1