We present and empirically test a novel approach for categorizing 3-D free form object shapesrepresented by range data . In contrast to traditional surface-signature based systems that use alignment to match specific objects, we adapted the newly introduced symbolic-signature representation to classify deformable shapes [10]. Our approach constructs anabstract description of shape classes using an ensemble of classifiers that learn object class parts and their corresponding geometrical relationships from a set of numeric and symbolic descriptors. We used our classification engine in a series of large scale discrimination experimentson two well-defined classes that share many common distinctive features. The experimental results suggest that our method outperforms traditional numeric signature-based methodologies.

We formulate linear dimensionality reduction as a semi-parametric estimation problem,enabling us to study its asymptotic behavior. We generalize the problem beyond additive Gaussian noise to (unknown) non-Gaussian additive noise, and to unbiased non-additive models.

Given a sentence, learning is first applied at word level to identify phrase candidates 0f the solution.

We will exemplify our reasoning using data from an experiment described in Sect.

Spectral methods for nonlinear dimensionality reduction (NLDR) impose a neighborhood graph on point data and compute eigenfunctions of a quadratic form generated from the graph.

Despite the popularity of connectionist models in cognitive science, their performance can often be difficult to evaluate. Inspired by the geometric approach to statistical model selection, we introduce a conceptually similar method to examine the global behavior of a connectionist model, by counting the number and types of response patterns it can simulate. The Markov Chain Monte Carlo-based algorithm that we constructed Þnds these patterns efficiently. We demonstrate the approach using two localist network models of speech perception.

We consider the general problem of learning from labeled and unlabeled data, which is often called semi-supervised learning or transductive inference. Aprincipled approach to semi-supervised learning is to design a classifying function which is sufficiently smooth with respect to the intrinsic structure collectively revealed by known labeled and unlabeled points. We present a simple algorithm to obtain such a smooth solution. Our method yields encouraging experimental results on a number of classification problemsand demonstrates effective use of unlabeled data.

This paper presents a method for learning a distance metric from relative comparisonsuch as "A is closer to B than A is to C". Taking a Support Vector Machine (SVM) approach, we develop an algorithm that provides a flexible way of describing qualitative training data as a set of constraints. We show that such constraints lead to a convex quadratic programming problem that can be solved by adapting standard methods forSVM training. We empirically evaluate the performance and the modelling flexibility of the algorithm on a collection of text documents.

We compute approximate analytical bootstrap averages for support vector classificationusing a combination of the replica method of statistical physics and the TAP approach for approximate inference. We test our method on a few datasets and compare it with exact averages obtained by extensive Monte-Carlo sampling.

We propose a nonlinear Canonical Correlation Analysis (CCA) method which works by coordinating or aligning mixtures of linear models. In the same way that CCA extends the idea of PCA, our work extends recent methodsfor nonlinear dimensionality reduction to the case where multiple embeddings of the same underlying low dimensional coordinates areobserved, each lying on a different high dimensional manifold. We also show that a special case of our method, when applied to only a single manifold, reduces to the Laplacian Eigenmaps algorithm. As with previous alignment schemes, once the mixture models have been estimated, all of the parameters of our model can be estimated in closed form without local optima in the learning. Experimental results illustrate the viability of the approach as a nonlinear extension of CCA.