Discretization of Parametrizable Signal Manifolds

TL;DR

This paper introduces algorithms for discretizing transformation manifolds to improve the efficiency and accuracy of transformation-invariant signal classification, demonstrating enhanced performance through optimized sampling strategies.

Contribution

It presents novel methods for selecting and jointly discretizing multiple manifolds to minimize distance estimation errors and optimize classification accuracy.

Findings

Sampling each manifold individually improves registration and classification accuracy.

Joint optimization of all samples further enhances classification performance.

Asymmetric sampling distribution across manifolds can increase accuracy.

Abstract

Transformation-invariant analysis of signals often requires the computation of the distance from a test pattern to a transformation manifold. In particular, the estimation of the distances between a transformed query signal and several transformation manifolds representing different classes provides essential information for the classification of the signal. In many applications the computation of the exact distance to the manifold is costly, whereas an efficient practical solution is the approximation of the manifold distance with the aid of a manifold grid. In this paper, we consider a setting with transformation manifolds of known parameterization. We first present an algorithm for the selection of samples from a single manifold that permits to minimize the average error in the manifold distance estimation. Then we propose a method for the joint discretization of multiple manifolds that represent different signal classes, where we optimize the transformation-invariant classification accuracy yielded by the discrete manifold representation. Experimental results show that sampling each manifold individually by minimizing the manifold distance estimation error outperforms baseline sampling solutions with respect to registration and classification accuracy. Performing an additional joint optimization on all samples improves the classification performance further. Moreover, given a fixed total number of samples to be selected from all manifolds, an asymmetric distribution of samples to different manifolds depending on their geometric structures may also increase the classification accuracy in comparison with the equal distribution of samples.