In machine learning, if the training data is an unbiased sample of an underlying distribution,
then the learned classification function will make accurate predictions for new samples …

This paper addresses classification tasks on a particular target domain in which labeled
training data are only available from source domains different from (but related to) the target …

This paper investigates domain generalization: How to take knowledge acquired from an
arbitrary number of related domains and apply it to previously unseen domains? We …

This paper presents our work on ACM MM Audio Visual Emotion Corpus 2014 (AVEC 2014)
using the baseline features in accordance with the challenge protocol. For prediction, we …

The combination of spectroscopic measurements and multivariate calibration techniques
(chemometrics) has become a state-of-the-art technology for process analytical chemistry …

We contribute, through this paper, to design a novel algorithm called maximum margin
projection with tensor representation (MMPTR). This algorithm is able to recognize gait and …

As graph technology is widely used in unsupervised dimensionality reduction, many
methods automatically construct a full connection graph to learn the structure of data, and …

We develop methodology for identifying and estimating sufficient reductions in regressions
with predictors that, given the response, follow a multivariate exponential family distribution …

Central subspaces have long been a key concept for sufficient dimension reduction. Initially
constructed for solving problems in the p< n setting, central subspace methods have seen …

Adopting continuous dimensional annotations for affective analysis has been gaining rising
attention by researchers over the past years. Due to the idiosyncratic nature of this problem …