Adaptive sparseness for supervised learning

Adaptive Sparseness for Supervised Learning

The goal of supervised learning is to infer a functional mapping based on a set of training examples. To achieve good generalization, it is necessary to control the “complexity” of the learned function. In Bayesian approaches, this is done by adopting a prior for the parameters of the function being learned. We propose a Bayesian approach to supervised learning, which leads to sparse solutions;...

An Adaptive Regularization Criterion for Supervised Learning

Sparseness-controlled Adaptive Algorithms for Echo Cancellation

In hands-free telephony, the acoustic coupling between the loudspeaker and the microphone generates echoes that can seriously degrade user experience. For this reason, effective Acoustic echo cancellation (AEC) is important to maintaining and improving the perceived voice quality of a call. Traditionally, adaptive filters have been deployed in acoustic echo cancellers to estimate the Acoustic i...

Adaptive Sparseness Using Jeffreys Prior

In this paper we introduce a new sparseness inducing prior which does not involve any (hyper)parameters that need to be adjusted or estimated. Although other applications are possible, we focus here on supervised learning problems: regression and classification. Experiments with several publicly available benchmark data sets show that the proposed approach yields state-of-the-art performance. I...

Semi-Supervised Learning with Adaptive Spectral Transform

This paper proposes a novel nonparametric framework for semi-supervised learning and for optimizing the Laplacian spectrum of the data manifold simultaneously. Our formulation leads to a convex optimization problem that can be efficiently solved via the bundle method, and can be interpreted as to asymptotically minimize the generalization error bound of semi-supervised learning with respect to ...