Sparse Correlation Kernel Analysis and Reconstruction
This paper presents a new paradigm for signal reconstruction and superresolution, Correlation Kernel Analysis (CKA), that is based on the selection of a sparse set of bases from a large dictionary of class- specific basis functions. The basis functions that we use are the correlation functions of th...
Main Authors: | Papgeorgiou, Constantine P., Girosi, Federico, Poggio, Tomaso |
---|---|
Language: | en_US |
Published: |
2004
|
Online Access: | http://hdl.handle.net/1721.1/7256 |
Similar Items
-
Extensions of a Theory of Networks for Approximation and Learning: Dimensionality Reduction and Clustering
by: Poggio, Tomaso, et al.
Published: (2004) -
Networks and the Best Approximation Property
by: Girosi, Federico, et al.
Published: (2004) -
Notes on PCA, Regularization, Sparsity and Support Vector Machines
by: Poggio, Tomaso, et al.
Published: (2004) -
A Theory of Networks for Appxoimation and Learning
by: Poggio, Tomaso, et al.
Published: (2004) -
Continuous Stochastic Cellular Automata that Have a Stationary Distribution and No Detailed Balance
by: Poggio, Tomaso, et al.
Published: (2004)