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 sub-structures 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.
- Non-negative matrix factorisation
- Sparse code shrinkage
ASJC Scopus subject areas
- Computer Vision and Pattern Recognition
- Artificial Intelligence