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Sparse deep-learning algorithm for recognition and categorisation

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4 Author(s)
Charalampous, K. ; Dept. of Production & Manage. Eng., Democritus Univ. Of Thrace, Xanthi, Greece ; Kostavelis, I. ; Amanatiadis, A. ; Gasteratos, A.

Presented is a deep-learning method for pattern classification and object recognition. The proposed methodology is based on an optimised version of the hierarchical temporal memory (HTM) algorithm and it preserves its basic structure, along with a tree structure of connected nodes. The tree structured scheme is inspired by the human neocortex, which provides great capabilities for recognition and categorisation. The proposed method is enriched with more representative quantisation centres using an adaptive neural gas algorithm, and a more accurate and dense grouping by applying a graph clustering technique. Sparse representation using L1 norm minimisation is embedded as a liaison between the quantisation centres and their grouping, reinforcing the proposed technique with advantages, such as a natural discrimination capability. The proposed work is experimentally compared with the aforementioned techniques as well as with state-of-the-art algorithms, presenting a better classification performance.

Published in:

Electronics Letters  (Volume:48 ,  Issue: 20 )