Flexible Online Representation Learning Based on Similarity Matching
Sparse high-dimensional representations are conducive to uncovering nontrivial structures in unsupervised exploration of data. Such a representation can deal with the dense connectivity in graphs relevant to community detection problems. However, sparse high-dimensional representations are capable of doing more, including manifold tiling and feature learning. Conventional algorithms optimize in the space of computationally intractable completely positive matrices or relax the problem to the space of doubly nonnegative matrices that scale with sample size in a way rendering them impractical for large data sets. Some of these methods also impose a row sum constraint, such as double stochast...
| Authors | Shagesh Sridharan, Yanis Bahroun, Anirvan M. Sengupta |
|---|---|
| Primary category | cs.LG |
| Published | 1 June 2026 |
| Last updated at source | 8 June 2026 |
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