What is the goal of sensory coding?
Neural Computation
Multi-frame compression: theory and design
Signal Processing - Special section on signal processing technologies for short burst wireless communications
Dictionary learning algorithms for sparse representation
Neural Computation
Learning Overcomplete Representations
Neural Computation
Multiclass Object Recognition with Sparse, Localized Features
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 1
Robust Object Recognition with Cortex-Like Mechanisms
IEEE Transactions on Pattern Analysis and Machine Intelligence
Sparse Coding Neural Gas for Analysis of Nuclear Magnetic Resonance Spectroscopy
CBMS '08 Proceedings of the 2008 21st IEEE International Symposium on Computer-Based Medical Systems
-SVD: An Algorithm for Designing Overcomplete Dictionaries for Sparse Representation
IEEE Transactions on Signal Processing
Matching pursuits with time-frequency dictionaries
IEEE Transactions on Signal Processing
Greed is good: algorithmic results for sparse approximation
IEEE Transactions on Information Theory
Stable recovery of sparse overcomplete representations in the presence of noise
IEEE Transactions on Information Theory
Signal Recovery From Random Measurements Via Orthogonal Matching Pursuit
IEEE Transactions on Information Theory
`Neural-gas' network for vector quantization and its application to time-series prediction
IEEE Transactions on Neural Networks
Approaching the Time Dependent Cocktail Party Problem with Online Sparse Coding Neural Gas
WSOM '09 Proceedings of the 7th International Workshop on Advances in Self-Organizing Maps
Multimodal Sparse Features for Object Detection
ICANN '09 Proceedings of the 19th International Conference on Artificial Neural Networks: Part II
Echo state networks with sparse output connections
ICANN'10 Proceedings of the 20th international conference on Artificial neural networks: Part I
Dictionary learning based on Laplacian score in sparse coding
MLDM'11 Proceedings of the 7th international conference on Machine learning and data mining in pattern recognition
Discriminative sparse coding on multi-manifolds
Knowledge-Based Systems
Pose-based human action recognition via sparse representation in dissimilarity space
Journal of Visual Communication and Image Representation
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We consider the problem of learning an unknown (overcomplete) basis from data that are generated from unknown and sparse linear combinations. Introducing the Sparse Coding Neural Gas algorithm, we show how to employ a combination of the original Neural Gas algorithm and Oja's rule in order to learn a simple sparse code that represents each training sample by only one scaled basis vector. We generalize this algorithm by using Orthogonal Matching Pursuit in order to learn a sparse code where each training sample is represented by a linear combination of up to k basis elements. We evaluate the influence of additive noise and the coherence of the original basis on the performance with respect to the reconstruction of the original basis and compare the new method to other state of the art methods. For this analysis, we use artificial data where the original basis is known. Furthermore, we employ our method to learn an overcomplete representation for natural images and obtain an appealing set of basis functions that resemble the receptive fields of neurons in the primary visual cortex. An important result is that the algorithm converges even with a high degree of overcompleteness. A reference implementation of the methods is provided.