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From neuronal cost-based metrics towards sparse coded signals classification

Sparse signal decomposition are keys to efficient compression, storage and denoising, but they lack appropriate methods to exploit this sparcity for a classification purpose. Sparse coding methods based on dictionary learning may result in spikegrams, a sparse and temporal representation of signals by a raster of kernel occurrence through time. This paper proposes a method for coupling spike train cost based metrics (from neuroscience) with a spikegram sparse decompositions for clustering multivariate signals. Experiments on character trajectories, recorded by sensors from natural handwriting, prove the validity of the approach, compared with currently available classification performance in literature.