In this paper, a multiple feature regularized kernel is proposed for hyperspectral imagery classification. To exploit the label information, a regularized kernel is used to refine the original kernel in the Support Vector Machine classifier. Furthermore, since spatial features have been widely investigated for hyperspectral imagery classification, different types of spatial features including spectral feature, local feature (i.e. local binary pattern), global feature (i.e. Gabor feature), and shape feature (i.e. extended multiattribute profiles) are included to provide distinguish discriminative information. Finally, a majority voting-based ensemble approach, which combines different types of features, is adopted to further increase the classification performance. Combining different discriminative feature information can improve the classification performance since one type of feature may result in poor performance, especially when the number of training samples is limited. Experimental results demonstrated that the proposed approach has superior performance compared with the state-of-the-art classifiers.

The majority of research in speech emotion recognition (SER) is conducted to recognize emotion categories. Recognizing dimensional emotion attributes is also important, however, and it has several advantages over categorical emotion. For this research, we investigate dimensional SER using both speech features and word embeddings. The concatenation network joins acoustic networks and text networks from bimodal features. We demonstrate that those bimodal features, both are extracted from speech, improve the performance of dimensional SER over unimodal SER either using acoustic features or word embeddings. A significant improvement on the valence dimension is contributed by the addition of word embeddings to SER system, while arousal and dominance dimensions are also improved. We proposed a multitask learning (MTL) approach for the prediction of all emotional attributes. This MTL maximizes the concordance correlation between predicted emotion degrees and true emotion labels simultaneously. The findings suggest that the use of MTL with two parameters is better than other evaluated methods in representing the interrelation of emotional attributes. In unimodal results, speech features attain higher performance on arousal and dominance, while word embeddings are better for predicting valence. The overall evaluation uses the concordance correlation coefficient score of the three emotional attributes. We also discuss some differences between categorical and dimensional emotion results from psychological and engineering perspectives.