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Multiview learning is more robust than single-view learning in many real applications. Canonical correlation analysis (CCA) is a popular technique to utilize information stemming from multiple feature sets. However, it does not exploit label information effectively. Later multiview linear discriminant analysis (MLDA) was proposed through combining CCA and linear discriminant analysis (LDA). Due to...
In this paper, we present a novel dimensionality reduction method, called sparse uncorrelated cross-domain feature extraction (SUFE), for signal classification in brain-computer interfaces (BCIs). Considering the differences between the source and target distributions of signals from different subjects, we construct an optimization objective which aims to find a projection matrix to transform the...
In the domain adaptation research, which recently becomes one of the most important research directions in machine learning, source and target domains are with different underlying distributions. In this paper, we propose an ensemble learning framework for domain adaptation. Owing to the distribution differences between source and target domains, the weights in the final model are sensitive to target...
A visual attention based approach is proposed to extract texts from complicated background in camera-based images. First, it applies the simplified visual attention model to highlight the region of interest (ROI) in an input image and to yield a map, named the VA map, consisting of the ROIs. Second, an edge map of image containing the edge information of four directions is obtained by Sobel operators...
For multi-view learning, existing methods usually exploit originally provided features for classifier training, which ignore the latent correlation between different views. In this paper, semantic features integrating information from multiple views are extracted for pattern representation. Canonical correlation analysis is used to learn the representation of semantic spaces where semantic features...
Energy is very important in electroencephalogram (EEG) signal classification. In this paper, a criterion called extreme energy difference (EED) is devised, which is a discriminative objective function to guide the process of spatially filtering EEG signals. The energy of the filtered EEG signals has the optimal discriminative capability under the EED criterion, and therefore EED can be considered...
This paper introduces an ensemble approach for electroencephalogram (EEG) signal classification, which aims to overcome the instability of the Fisher discriminant feature extractor for brain-computer interface (BCI) applications. Through the random selection of electrodes from candidate electrodes, multiple individual classifiers are constructed. In a feature subspace determined by a couple of randomly...
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