刊物主题:Physics Complexity Artificial Intelligence and Robotics Electronic and Computer Engineering Operation Research and Decision Theory
出版者:Springer Netherlands
ISSN:1573-773X
卷排序:44
文摘
We address the problem of unsupervised visual domain adaptation for transferring category models from one visual domain or image data set to another. We present a new unsupervised domain adaptation algorithm based on subspace alignment. The core idea of our approach is to reduce the discrepancy between the source domain and the target domain in a latent discriminative subspace. Specifically, we first generate pseudo-labels for the target data by applying spectral clustering to a cross-domain similarity matrix, which is built from sparse coefficients found in a low-dimensional latent space. This coarse alignment between the two domains exploits the assumption that the collection of data of different classes from both domains can be viewed as samples from a union of low-dimensional subspaces. Then, we create discriminative subspaces for both domains using partial least squares correlation. Finally, a mapping which aligns the discriminative source subspace into the target one is learned by minimizing a Bregman matrix divergence function. Experimental results on benchmark cross-domain visual object recognition data sets and cross-view scene classification data sets demonstrate that the proposed method outperforms the baselines and several state-of-the-art competing methods.
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