Objective. Latest target recognition methods that are equipped with learning from the
subject's calibration data, represented by the extended canonical correlation analysis …

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 …

In general, a large amount of training data can effectively improve the classification
performance of the Steady-State Visually Evoked Potential (SSVEP)-based Brain-Computer …

This paper presents our work on ACM MM Audio Visual Emotion Corpus 2013 (AVEC 2013)
depression recognition sub-challenge using the baseline features in accordance with the …

The feasibility of a steady-state visual evoked potential (SSVEP) brain–computer interface
(BCI) with a single-flicker stimulus for multiple-target decoding has been demonstrated in a …

The canonical correlation analysis (CCA) aims at measuring linear relationships between
two sets of variables (views) that can be used for feature extraction in classification problems …

In the classification of non-stationary time series data such as sounds, it is often tedious and
expensive to get a training set that is representative of the target concept. To alleviate this …

Canonical correlation analysis (CCA) is an effective way to find two appropriate subspaces
in which Pearson's correlation coefficients are maximized between projected random …

The selection of multi-view features plays an important role for classifying multi-view data,
especially the data with high dimension. In this paper, a novel multi-view feature selection …

In many supervised learning problems, determining the true labels of training instances is
expensive, laborious, and even practically impossible. As an alternative approach, it is much …