697 research outputs found
Mismatch in the Classification of Linear Subspaces: Sufficient Conditions for Reliable Classification
This paper considers the classification of linear subspaces with mismatched
classifiers. In particular, we assume a model where one observes signals in the
presence of isotropic Gaussian noise and the distribution of the signals
conditioned on a given class is Gaussian with a zero mean and a low-rank
covariance matrix. We also assume that the classifier knows only a mismatched
version of the parameters of input distribution in lieu of the true parameters.
By constructing an asymptotic low-noise expansion of an upper bound to the
error probability of such a mismatched classifier, we provide sufficient
conditions for reliable classification in the low-noise regime that are able to
sharply predict the absence of a classification error floor. Such conditions
are a function of the geometry of the true signal distribution, the geometry of
the mismatched signal distributions as well as the interplay between such
geometries, namely, the principal angles and the overlap between the true and
the mismatched signal subspaces. Numerical results demonstrate that our
conditions for reliable classification can sharply predict the behavior of a
mismatched classifier both with synthetic data and in a motion segmentation and
a hand-written digit classification applications.Comment: 17 pages, 7 figures, submitted to IEEE Transactions on Signal
Processin
Compressive Classification
This paper derives fundamental limits associated with compressive
classification of Gaussian mixture source models. In particular, we offer an
asymptotic characterization of the behavior of the (upper bound to the)
misclassification probability associated with the optimal Maximum-A-Posteriori
(MAP) classifier that depends on quantities that are dual to the concepts of
diversity gain and coding gain in multi-antenna communications. The diversity,
which is shown to determine the rate at which the probability of
misclassification decays in the low noise regime, is shown to depend on the
geometry of the source, the geometry of the measurement system and their
interplay. The measurement gain, which represents the counterpart of the coding
gain, is also shown to depend on geometrical quantities. It is argued that the
diversity order and the measurement gain also offer an optimization criterion
to perform dictionary learning for compressive classification applications.Comment: 5 pages, 3 figures, submitted to the 2013 IEEE International
Symposium on Information Theory (ISIT 2013
Détection et Reconnaissance des Gestes Emblématiques
Session "Atelier IHMA"National audienceDans cette contribution, nous présentons un système de reconnaissance en ligne de gestes emblématiques et son utilisation pour le contrôle d'un robot mobile. Ce système comporte quatre sous-systèmes : un premier qui permet de détecter la personne et d'extraire les mouvements de la partie supérieure de cette personne. Un second, permet d'isoler les mouvements, une troisième permet de reconnaître un des mouvements appris a priori. Enfin le dernier module, traduit les mouvements reconnus en termes de contrôle d'un robot mobile à roues. Dans notre approche, nous avons surtout traité du problème de la généralisation : faire l'apprentissage sur une base réduite de personnes et utiliser cette connaissance pour reconnaître n'importe quelle personne, indépendamment de sa morphologie, de son âge, de son sexe et de son positionnement par rapport au capteur. Nous détaillons les résultats obtenus pour la reconnaissance ainsi que l'utilisation du système dans des scenarios de contrôle d'un robot
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