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 ▲]

Supervised linear feature extraction for mobile robot localization

in Proc. IEEE Int. Conf. on Robotics and Automation (2000)

We are seeking linear projections of supervised high-dimensional robot observations and an appropriate environment model that optimize the robot localization task. We show that an appropriate risk ... [more ▼]

We are seeking linear projections of supervised high-dimensional robot observations and an appropriate environment model that optimize the robot localization task. We show that an appropriate risk function to minimize is the conditional entropy of the robot positions given the projected observations. We propose a method of iterative optimization through a probabilistic model based on kernel smoothing. To obtain good starting optimization solutions we use canonical correlation analysis. We apply our method on a real experiment involving a mobile robot equipped with an omnidirectional camera in an office setup. [less ▲]

Robot environment modeling via principal component regression

in Proc. of Intelligent Robots and Systems, International Conference on (1999)

A key issue in mobile robot applications involves building a map of the environment to be used by the robot for localization and path planning. We propose a framework for robot map building which is based ... [more ▼]

A key issue in mobile robot applications involves building a map of the environment to be used by the robot for localization and path planning. We propose a framework for robot map building which is based on principal component regression, a statistical method for extracting low-dimensional dependencies between a set of input and target values. A supervised set of robot positions (inputs) and associated high-dimensional sensor measurements (targets) are assumed. A set of globally uncorrelated features of the original sensor measurements are obtained by applying principal component analysis on the target set. A parametrized model of the conditional density function of the sensor features given the robot positions is built based on an unbiased estimation procedure that fits interpolants for both the mean and the variance of each feature independently. The simulation results show that the average Bayesian localization error is an increasing function of the principal component index. [less ▲]

An information-theoretic localization criterion for robot map building

in Proc. ACAI'99, Int. Conf. on Machine Learning and Applications (1999)

Mixture density estimation based on Maximum Likelihood and test statistics

in Neural Processing Letters (1999), 9(1), 63-76

Dynamic Sensory Probabilistic Maps for Mobile Robot Localization

in Proc. IEEE/RSJ Int. Conf. on Intelligent Robots and Systems (1998)

Global Path Planning for Autonomous Qualitative Navigation

in Proc. 8th IEEE Int. Conf. on Tools with AI (1996)