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Based on the Bayesian learning principle (BayesMSDA), this paper presents a new multi-source domain adaptation frame work, where one target domain and more than one source do mains are used. In this framework, the label of a target data point is determined according to its posterior probability, which is calculated using the Bayesian formula. To fulfill this frame work, a novel prior of the target domain based on Laplacian matrix and a new likelihood that is dynamically obtained us ing the k-nearest neighbors of a data point are defined. We focus on the situation that there are no labeled data obtained from the target domain while most of them are from source domains. Experiments on synthetic data and real-world data illustrate that our framework has a good performance.

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In this paper, we present a novel dimensional ity 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 difl"erent subjects, we construct an optimization objective which aims to find a projection matrix to transform the original data in a high dimensional space into a low-dimensional latent space. In the low-dimensional space, both the discrimination of different classes and transferability between the source and target domains are preserved. To make sure the minimum information redundancy, the extracted features are designed to be statistically uncorrelated.Then, by adding the ll-norm penalty, we incorporate sparsity into the uncorrelated transformation. In the experiments, we evaluate the method with multiple datasets, and compare with the state of-the-art methods. The results show that the proposed approach has better performance and is suitable for cross-domain signal classification.