Learning invariance from transformation sequences
Neural Computation
What is the goal of sensory coding?
Neural Computation
Perception as Bayesian inference
Perception as Bayesian inference
Convolutional networks for images, speech, and time series
The handbook of brain theory and neural networks
Estimation with Applications to Tracking and Navigation
Estimation with Applications to Tracking and Navigation
Slow feature analysis: unsupervised learning of invariances
Neural Computation
Convex Optimization
Self-taught learning: transfer learning from unlabeled data
Proceedings of the 24th international conference on Machine learning
LIBSVM: A library for support vector machines
ACM Transactions on Intelligent Systems and Technology (TIST)
Second-order complex random vectors and normal distributions
IEEE Transactions on Signal Processing
Stable recovery of sparse overcomplete representations in the presence of noise
IEEE Transactions on Information Theory
Just relax: convex programming methods for identifying sparse signals in noise
IEEE Transactions on Information Theory
Unsupervised analysis of polyphonic music by sparse coding
IEEE Transactions on Neural Networks
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Sparse coding has established itself as a useful tool for the representation of natural data in the neuroscience as well as signal-processing literature. The aim of this letter, inspired by the human brain, is to improve on the performance of the sparse coding algorithm by trying to bridge the gap between neuroscience and engineering. To this end, we build on the localized perception-action cycle in cognitive neuroscience by categorizing it under the umbrella of perceptual attention, which lends itself to increase gradually the contrast between relevant information and irrelevant information. Stated in another way, irrelevant information is filtered away, while relevant information about the environment is enhanced from one cycle to the next. We may thus think in terms of the information filter, which, in a Bayesian context, was introduced in the literature by Fraser 1967. In a Bayesian context, the information filter provides a method for algorithmic implementation of perceptual attention. The information filter may therefore be viewed as the basis for improving the algorithmic performance of sparse coding. To support this performance improvement, the letter presents two computer experiments. The first experiment uses simulated real-valued data that are generated to purposely make the problem challenging. The second uses real-life radar data that are complex valued, hence the proposal to introduce Wirtinger calculus into derivation of the new algorithm.