Preprocessing for classification of sparse data: application to trajectory recognition

International audienceOn one hand, sparse coding, which is widely used in signal processing, consists of representing signals as linear combinations of few elementary patterns selected from a dedicated dictionary. The output is a sparse vector containing few coding coefficients and is called sparse code. On the other hand, Multilayer Perceptron (MLP) is a neural network classification method that learns non linear borders between classes using labeled data examples. The MLP input data are vectors, usually normalized and preprocessed to minimize the inter-class correlation. This article acts as a link between sparse coding and MLP by converting sparse code into convenient vectors for MLP input. This original association assures in this way the classification of any sparse signals. Experimental results obtained by the whole process on trajectories data and comparisons to other methods show that this approach is efficient for signals classification

Appariement Robuste de Formes Visuelles Complexes, Application à la Détection d'Objets

The increase in computer resources, associated with the advent of powerful classification methods such as AdaBoost and neural networks allow current object detection systems to reach high detection rates. However those methods require a large training database of several thousands of examples. This paper presents an object detection method that gives state-of-the-art results, while using a reduced training database. First, we present the various methods used in object detection systems, and particularly the machines learning methods involved. Then, we explain a detection system based on correlation and using a database of less than one hundred images. This system allowed us to develop a method of association of similarity measures using orthogonal edges filters obtain using a method derived from the PCA. We then show that we can develop a face detection system able to work with a small number of examples. The similarity measure based on correlation is the most limiting factor of our detection system; that's why we replaced it by a Multilayer Perceptron. We then applied the association of filtered images to the new similarity measure and showed an improvement in detection rate using a small learning database. Finally, we highlight the possible solutions able to improve the detection system speed and to decrease the number of learning examples.L'augmentation des moyens informatiques associée à l'avènement de méthodes de classification performantes tels que l'AdaBoost ou les réseaux de neurones ont permis d'obtenir des systèmes de détection d'objets efficaces, mais nécessitant l'annotation manuelle de plusieurs milliers d'images exemples. Ce document présente une méthode permettant d'obtenir un système de détection d'objets capable de fonctionner avec une base d'images exemples de dimension réduite, tout en obtenant les taux de détection de l'état de l'art en détection de visages. Nous commençons par présenter les diverses méthodes utilisées en détection d'objets, et en particulier, les méthodes d'apprentissages associées. Puis, nous expliquons un système de détection basé sur la corrélation et fonctionnant avec une base d'exemples de moins d'une centaine d'images. Ce système nous a permis de mettre au point une méthode d'association de mesures de similarité utilisant des filtres de contours orientés orthogonaux. Les filtres sont obtenus par une méthode dérivée de la PCA qui permet de calculer des filtres orthogonaux adaptés à la classe d'objets à détecter. Nous montrons alors qu'il est possible de mettre au point un système de détection de visages fonctionnel avec très peu d'exemples. La corrélation s'avérant le facteur limitant le plus les résultats, nous avons ensuite remplacé cette dernière par un Perceptron Multicouche. Nous avons appliqué les méthodes d'associations d'images de contours orientés et montré une nette amélioration des taux de détection en utilisant des bases d'apprentissages de dimension réduite. Finalement, nous mettons en évidence les perspectives et solutions possibles qui nous permettraient de minimiser encore le nombre d'exemples d'apprentissage

Mirror extreme BMI phenotypes associated with gene dosage at the chromosome 16p11.2 locus

Both obesity and being underweight have been associated with increased mortality. Underweight, defined as a body mass index (BMI) ≤ 18.5 kg per m(2) in adults and ≤ -2 standard deviations from the mean in children, is the main sign of a series of heterogeneous clinical conditions including failure to thrive, feeding and eating disorder and/or anorexia nervosa. In contrast to obesity, few genetic variants underlying these clinical conditions have been reported. We previously showed that hemizygosity of a ∼600-kilobase (kb) region on the short arm of chromosome 16 causes a highly penetrant form of obesity that is often associated with hyperphagia and intellectual disabilities. Here we show that the corresponding reciprocal duplication is associated with being underweight. We identified 138 duplication carriers (including 132 novel cases and 108 unrelated carriers) from individuals clinically referred for developmental or intellectual disabilities (DD/ID) or psychiatric disorders, or recruited from population-based cohorts. These carriers show significantly reduced postnatal weight and BMI. Half of the boys younger than five years are underweight with a probable diagnosis of failure to thrive, whereas adult duplication carriers have an 8.3-fold increased risk of being clinically underweight. We observe a trend towards increased severity in males, as well as a depletion of male carriers among non-medically ascertained cases. These features are associated with an unusually high frequency of selective and restrictive eating behaviours and a significant reduction in head circumference. Each of the observed phenotypes is the converse of one reported in carriers of deletions at this locus. The phenotypes correlate with changes in transcript levels for genes mapping within the duplication but not in flanking regions. The reciprocal impact of these 16p11.2 copy-number variants indicates that severe obesity and being underweight could have mirror aetiologies, possibly through contrasting effects on energy balance