Search results for "dimensionality"

2004

This paper presents the use of Support Vector Machines (SVMs) for prediction and analysis of antisense oligonucleotide (AO) efficacy. The collected database comprises 315 AO molecules including 68 features each, inducing a problem well-suited to SVMs. The task of feature selection is crucial given the presence of noisy or redundant features, and the well-known problem of the curse of dimensionality. We propose a two-stage strategy to develop an optimal model: (1) feature selection using correlation analysis, mutual information, and SVM-based recursive feature elimination (SVM-RFE), and (2) AO prediction using standard and profiled SVM formulations. A profiled SVM gives different weights to …

ERP denoising in multichannel EEG data using contrasts between signal and noise subspaces

2009

Abstract In this paper, a new method intended for ERP denoising in multichannel EEG data is discussed. The denoising is done by separating ERP/noise subspaces in multidimensional EEG data by a linear transformation and the following dimension reduction by ignoring noise components during inverse transformation. The separation matrix is found based on the assumption that ERP sources are deterministic for all repetitions of the same type of stimulus within the experiment, while the other noise sources do not obey the determinancy property. A detailed derivation of the technique is given together with the analysis of the results of its application to a real high-density EEG data set. The inter…

Multi-class pairwise linear dimensionality reduction using heteroscedastic schemes

2010

Accepted version of an article published in the journal: Pattern Recognition. Published version on Sciverse: http://dx.doi.org/10.1016/j.patcog.2010.01.018 Linear dimensionality reduction (LDR) techniques have been increasingly important in pattern recognition (PR) due to the fact that they permit a relatively simple mapping of the problem onto a lower-dimensional subspace, leading to simple and computationally efficient classification strategies. Although the field has been well developed for the two-class problem, the corresponding issues encountered when dealing with multiple classes are far from trivial. In this paper, we argue that, as opposed to the traditional LDR multi-class schemes…

Part-of-Speech Induction by Singular Value Decomposition and Hierarchical Clustering

2006

Part-of-speech induction involves the automatic discovery of word classes and the assignment of each word of a vocabulary to one or several of these classes. The approach proposed here is based on the analysis of word distributions in a large collection of German newspaper texts. Its main advantage over other attempts is that it combines the hierarchical clustering of context vectors with a previous step of dimensionality reduction that minimizes the effects of sampling errors.

Anomaly Detection from Network Logs Using Diffusion Maps

2011

The goal of this study is to detect anomalous queries from network logs using a dimensionality reduction framework. The fequencies of 2-grams in queries are extracted to a feature matrix. Dimensionality reduction is done by applying diffusion maps. The method is adaptive and thus does not need training before analysis. We tested the method with data that includes normal and intrusive traffic to a web server. This approach finds all intrusions in the dataset. peerReviewed

A Constrained Band Selection Method Based on Information Measures for Spectral Image Color Visualization

2011

International audience; We present a new method for the visualization of spectral images, based on a selection of three relevant spectral channels to build a Red-Green-Blue composite. Band selection is achieved by means of information measures at the first, second and third orders. Irrelevant channels are preliminarily removed by means of a center-surround entropy comparison. A visualization-oriented spectrum segmentation based on the use of color matching functions allows for computational ease and adjustment of the natural rendering. Results from the proposed method are presented and objectively compared to four other dimensionality reduction techniques in terms of naturalness and informa…

Spatially variant dimensionality reduction for the visualization of multi/hyperspectral images

2011

International audience; In this paper, we introduce a new approach for color visu- alization of multi/hyperspectral images. Unlike traditional methods, we propose to operate a local analysis instead of considering that all the pixels are part of the same population. It takes a segmentation map as an input and then achieves a dimensionality reduction adaptively inside each class of pixels. Moreover, in order to avoid unappealing discon- tinuities between regions, we propose to make use of a set of distance transform maps to weigh the mapping applied to each pixel with regard to its relative location with classes' centroids. Results on two hyperspec- tral datasets illustrate the efficiency of…

Saliency in spectral images

2011

International audience; Even though the study of saliency for color images has been thoroughly investigated in the past, very little attention has been given to datasets that cannot be displayed on traditional computer screens such as spectral images. Nevertheless, more than a means to predict human gaze, the study of saliency primarily allows for measuring infor- mative content. Thus, we propose a novel approach for the computation of saliency maps for spectral images. Based on the Itti model, it in- volves the extraction of both spatial and spectral features, suitable for high dimensionality images. As an application, we present a comparison framework to evaluate how dimensionality reduct…

Multidimensionality in host manipulation mimicked by serotonin injection.

2014

Manipulative parasites often alter the phenotype of their hosts along multiple dimensions. ‘Multidimensionality’ in host manipulation could consist in the simultaneous alteration of several physiological pathways independently of one another, or proceed from the disruption of some key physiological parameter, followed by a cascade of effects. We compared multidimensionality in ‘host manipulation’ between two closely related amphipods, Gammarus fossarum and Gammarus pulex, naturally and experimentally infected with Pomphorhynchus laevis (Acanthocephala), respectively. To that end, we calculated in each host–parasite association the effect size of the difference between infected and uninfect…

Measuring vine leaf roughness by image processing

2013

International audience; In precision spraying, spray application efficiency depends on the pesticide application method, the phytosanitary product as well as the leaf surface properties. For environmental and economic reasons, the global trend is to reduce the pesticide application rate of the few approved active substances. Under these constraints, one of the challenges is to improve the efficiency of pesticide application. Different parameters can influence on pesticide application as nozzle types, liquid viscosity and leaf surface. Specific models have been developed showing that the predominant factor for the leaf is the leaf roughness, because it is related on adhesion mechanisms of li…