Name
Abstract | ||
|---|---|---|
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. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1007/s11063-015-9494-6 | Neural Processing Letters |
Keywords | Field | DocType |
Unsupervised domain adaptation,Sparse subspace clustering,Partial least square correlation,Subspace alignment | Spectral clustering,Data set,Subspace topology,Pattern recognition,Matrix (mathematics),Partial least squares regression,Linear subspace,Artificial intelligence,Discriminative model,Machine learning,Mathematics,Cognitive neuroscience of visual object recognition | Journal |
Volume | Issue | ISSN |
44 | 3 | 1370-4621 |
Citations | PageRank | References |
3 | 0.38 | 16 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Hao Sun | 1 | 56 | 7.07 |
| Shuai Liu | 2 | 105 | 29.14 |
| Shilin Zhou | 3 | 72 | 13.94 |