Sparse Coding and Selected Applications

Jens Hocke; Kai Labusch; Erhardt Barth; Thomas Martinetz

doi:10.1007/s13218-012-0197-0

Sparse Coding and Selected Applications

Jens Hocke, Kai Labusch, Erhardt Barth, Thomas Martinetz

Institute of Neuro- and Bioinformatics

2 Citations (Scopus)

Abstract

Sparse coding has become a widely used framework in signal processing and pattern recognition. After a motivation of the principle of sparse coding we show the relation to Vector Quantization and Neural Gas and describe how this relation can be used to gen-eralize Neural Gas to successfully learn sparse coding dictionaries. We explore applications of sparse coding to image-feature extraction, image reconstruction and deconvolution, and blind source separation.

Original language	English
Title of host publication	KI - Künstliche Intelligenz
Number of pages	7
Publication date	09.05.2012
Pages	349-355
ISBN (Print)	4515005502
DOIs	https://doi.org/10.1007/s13218-012-0197-0
Publication status	Published - 09.05.2012

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 3 Good Health and Well-being
SDG 9 Industry, Innovation, and Infrastructure

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10.1007/s13218-012-0197-0

HoLaBaMa12Final published version, 948 KB

Cite this

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Sparse Coding and Selected Applications

Abstract

UN SDGs

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SPP 1527, Subproject: Learning Efficient Sensing for Active Vision (Esensing)

Cite this

Sparse Coding and Selected Applications

Abstract

UN SDGs

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Other files and links

Fingerprint

Projects

SPP 1527, Subproject: Learning Efficient Sensing for Active Vision (Esensing)

Cite this