Learning Doubly Sparse Explicitly Conditioned Transforms
Finding convenient spaces in which certain hypotheses regarding an assumed sparse structure of natural signals hold true has become a desirable result in recent research, its implications being reflected in areas such as data compression, noise reduction and feature extraction. While the extensively used analytical transforms, such as DFT or DCT, already provide efficient algorithms and robust sparse representations, they assume a fixed prior about the data, failing to accurately capture the specific structure of more restrictive classes of signals. To address this, the concept of a data-adaptive, learnt transform has been introduced in the literature, allowing for the reduction of a resi...
| Authors | Tudor Pistol |
|---|---|
| Primary category | math.OC |
| Published | 9 June 2026 |
| Last updated at source | 9 June 2026 |
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