One-shot data clustering mechanism using a distributed associative memory scheme for on-site recognition within network of smart objects

Reduced-Distributed Hierarchical Graph Neuron (R-DHGN) is a one-shot learning distributed associative memory algorithm for data classification, which reduces the computational complexity of existing recognition algorithms by distributing the recognition process into smaller processing clusters. This paper investigates an effect of unsupervised one-shot learning mechanism for data classification within a computational network. This computational network may represent a network of objects than can be deployed in the existing Internet-of-Things (IoT) environment that offers seamless connectivity between smart devices such as sensors. Our approach extends the pattern recognition capability of Distributed Hierarchical Graph Neuron (DHGN). The interprocess communications of DHGN scheme is significantly reduced, and preliminary results obtained from the series of comparative analyses with other established classifiers have indicated the capability of R-DHGN to produce one-shot classification technique using a lightweight recognition mechanism. Simple dataset of iris plants have been used to demonstrate such capability of R-DHGN.