Abstract
Spectral clustering can provide surprising performances. As all kernel methods, is uses a similarity matrix, whose size grows with n 2, and it requires to solve a possibly large eigenproblem. In this paper we focus on a method for spectral embedding of stream data, modeled as an unbounded quantity of input observation. A second purpose of this work is to analyze the proposed method and compare it with traditional neural network implementations: current knowledge about computations in neurons and the brain does not contrast with the computing primitives required for a local implementation of the proposed technique. A hypothesis stemming from this work could be that concept formation and discrimination in neurons and the brain could be explained by a spectral embedding framework.
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Rovetta, S., Masulli, F. (2015). Online Spectral Clustering and the Neural Mechanisms of Concept Formation. In: Bassis, S., Esposito, A., Morabito, F. (eds) Advances in Neural Networks: Computational and Theoretical Issues. Smart Innovation, Systems and Technologies, vol 37. Springer, Cham. https://doi.org/10.1007/978-3-319-18164-6_7
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DOI: https://doi.org/10.1007/978-3-319-18164-6_7
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