Wittmeyer's pseudoinverse iterative algorithm is formulated as a dynamic connectionist Data Compression and Reconstruction (DCR) network, and subnets of this type are supplemented by the winner-take-all paradigm. The winner is selected upon the goodness-of-fit of the input reconstruction. The network can be characterised as a competitive-cooperative-competitive architecture by virtue of the contrast enhancing properties of the pseudoinverse subnets. The network is capable of fast learning. The adopted learning method gives rise to increased sampling in the vicinity of dubious boundary regions that resembles the phenomenon of categorical perception. The generalising abilities of the scheme allow one to utilise single bit connection strengths. The network is robust against input noise and contrast levels, shows little sensitivity to imprecise connection strengths, and is promising for mixed VLSI implementation with on-chip learning properties. The features of the DCR network are demonstrated on the NIST database of handprinted characters.
- Categorical perception
- Competitive-cooperative behaviour
- Hand-printed character recognition
- On-chip learning
- Pseudoinverse method
ASJC Scopus subject areas
- Artificial Intelligence