Robust hierarchical image representation using non-negative matrix factorisation with sparse code shrinkage preprocessing

doi:10.1007/s10044-002-0185-3

Robust hierarchical image representation using non-negative matrix factorisation with sparse code shrinkage preprocessing

2003-12-01

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AI

DOI：10.1007/s10044-002-0185-3

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When analysing patterns, our goals are (i) to find structure in the presence of noise, (ii) to decompose the observed structure into sub-components, and (iii) to use the components for pattern completion. Here, a novel loop architecture is introduced to perform these tasks in an unsupervised manner. The architecture combines sparse code shrinkage with non-negative matrix factorisation, and blends their favourable properties: sparse code shrinkage aims to remove Gaussian noise in a robust fashion; non-negative matrix factorisation extracts substructures from the noise filtered inputs. The loop architecture performs robust pattern completion when organised into a two-layered hierarchy. We demonstrate the power of the proposed architecture on the so-called 'bar-problem' and on the FERET facial database.

Keywords:

hierarchy

non-negative matrix factorisation

sparse code shrinkage

Robust hierarchical image representation using non-negative matrix factorisation with sparse code shrinkage preprocessing

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