Sparse Approximate Solutions to Linear Systems
SIAM Journal on Computing
Colorization using optimization
ACM SIGGRAPH 2004 Papers
Non-linear Filter Response Distributions of Natural Colour Images
Computational Color Imaging
| Hi-index | 0.00 |
In this article we establish a connection between semi-supervised learning and compressive sampling. We show that sparsity and compressibility of the learning function can be obtained from heavy-tailed distributions of filter responses or coefficients in spectral decompositions. In many cases the NP-hard problems of finding sparsest solutions can be replaced by l1-problems from convex optimisation theory, which provide effective tools for semi-supervised learning. We present several conjectures and examples.