Evaluating Complex Sparse Representation of Hypervectors for Unsupervised Machine Learning

The increasing use of Vector Symbolic Architectures (VSA) in machine learning has contributed towards en-ergy efficient computation, short training cycles and improved performance. A further advancement of VSA is to leverage sparse representations, where the VSA-encoded hypervectors are sparsified to represent receptive field properties when encoding sensory inputs. The hyperseed algorithm is an unsupervised machine learning algorithm based on VSA for fast learning a topology preserving feature map of unlabelled data. In this paper, we implement two methods of sparse block-codes on the hyperseed algorithm, they are selecting the maximum element of each block and selecting a random element of each block as the nonzero element. Finally, the sparsified hyperseed algorithm is empirically evaluated for performance using three distinct bench-mark datasets, Iris classification, classification and visualisation of synthetic datasets from the Fundamental Clustering Problems Suite and language classification using n-gram statistics.