Supervised dimension reduction of intrinsically low-dimensional data

in Neural Computation (2002), 14(1), 191-215

High-dimensional data generated by a system with limited degrees of freedom are often constrained in low-dimensional manifolds in the original space. In this article, we investigate dimension-reduction ... [more ▼]

High-dimensional data generated by a system with limited degrees of freedom are often constrained in low-dimensional manifolds in the original space. In this article, we investigate dimension-reduction methods for such intrinsically low-dimensional data through linear projections that preserve the manifold structure of the data. For intrinsically one-dimensional data, this implies projecting to a curve on the plane with as few intersections as possible. We are proposing a supervised projection pursuit method that can be regarded as an extension of the single-index model for nonparametric regression. We show results from a toy and two robotic applications. [less ▲]

Efficient source adaptivity in independent component analysis

in IEEE Transactions on Neural Networks (2001), 12(3), 559-566

A basic element in most independent component analysis (ICA) algorithms is the choice of a model for the score functions of the unknown sources. While this is usually based on approximations, for large ... [more ▼]

A basic element in most independent component analysis (ICA) algorithms is the choice of a model for the score functions of the unknown sources. While this is usually based on approximations, for large data sets it is possible to achieve "source adaptivity" by directly estimating from the data the "'true" score functions of the sources. In this paper we describe an efficient scheme for achieving this by extending the fast density estimation method of Silverman (1982), We show with a real and a synthetic experiment that our method can provide more accurate solutions than state-of-the-art methods when optimization is carried out in the vicinity of the global minimum of the contrast function. [less ▲]

A probabilistic model for appearance-based robot localization

in Image and Vision Computing (2001), 19(6), 381-391

In this paper we present a method for an appearance-based modeling of the environment of a mobile robot. We describe the task (localization of the robot) in a probabilistic framework. Linear image ... [more ▼]

In this paper we present a method for an appearance-based modeling of the environment of a mobile robot. We describe the task (localization of the robot) in a probabilistic framework. Linear image features are extracted using a Principal Component Analysis. The appearance model is represented as a probability density function of the image feature vector given the location of the robot. We estimate this density model from the data with a kernel estimation method. We show how the parameters of the model influence the localization performance. We also study how many features and which features are needed for good localization. (C) 2001 Elsevier Science B.V. All rights reserved. [less ▲]

Appearance-Based Robot Localization

in Proc. IJCAI'99, 16th Int. Joint Conf. on Artificial Intelligence, ROB-2 Workshop (1999)