Name
Abstract | ||
|---|---|---|
In this paper, we address two challenging problems in unsupervised subspace learning: 1) how to automatically identify the feature dimension of the learned subspace (i.e., automatic subspace learning) and 2) how to learn the underlying subspace in the presence of Gaussian noise (i.e., robust subspace learning). We show that these two problems can be simultaneously solved by proposing a new method ... |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1109/TCYB.2016.2572306 | IEEE Transactions on Cybernetics |
Keywords | Field | DocType |
Robustness,Learning systems,Gaussian noise,Training data,Principal component analysis,Linear programming,Estimation | Manifold structure,Prime (order theory),Training set,Mathematical optimization,Embedding,Subspace topology,Projection (linear algebra),Linear subspace,Artificial intelligence,Gaussian noise,Machine learning,Mathematics | Journal |
Volume | Issue | ISSN |
47 | 11 | 2168-2267 |
Citations | PageRank | References |
38 | 0.84 | 61 |
Authors | ||
4 | ||