Consider a domain-adaptive supervised learning setting, where classifier learns from labeled data in source domain and unlabeled target to predict the corresponding labels. If classifier’s assumption on relationship between domains (e.g. covariate shift, common subspace, etc.) is valid, then it will usually outperform non-adaptive classifier. its invalid, can perform substantially worse. Valida...

Inspired by the definition of symmetric decomposition, we introduce the concept of shape partition of a tensor and formulate a general tensor spectral problem that includes all the relevant spectral problems as special cases. We formulate irreducibility and symmetry properties of a nonnegative tensor T in terms of the associated shape partition. We recast the spectral problem for T as a fixed p...

We show that Gabor lter representations of facial images give quantitatively indistinguishable results for classi cation of facial expressions as local PCA representations, in contrast to other recent work. We then show that a linear discriminant analysis performed on the Gabor lter representation automatically locates the important regions corresponding to the facial actions involved in portra...

We present the analysis performed over eleven speakers (five women and 6 men) in order to obtain the most important parameters as far as speaker identity is concerned. Parameters that have been studied are F0, six formants, five bandwidths and four source parameters. Feature selection is based on linear discriminant analysis. Results show that the most relevant parameter is F0, followed by form...

The authors consider a robust linear discriminant function based on high breakdown location and covariance matrix estimators. They derive influence functions for the estimators of the parameters of the discriminant function and for the associated classification error. The most B-robust estimator is determined within the class of multivariate S-estimators. This estimator, which minimizes the max...