Abstract
Dealing with time-varying high dimensional data is a big problem for real time pattern recognition. Only linear projections, like principal component analysis, are used in real time while nonlinear techniques need the whole database (offline). Their incremental variants do no work properly. The onCCA neural network addresses this problem; it is incremental and performs simultaneously the data quantization and projection by using the Curvilinear Component Analysis (CCA), a distance-preserving reduction technique. However, onCCA requires an initial architecture, provided by a small offline CCA. This paper presents a variant of onCCA, called growing CCA (GCCA), which has a self-organized incremental architecture adapting to the nonstationary data distribution. This is achieved by introducing the ideas of “seeds”, pairs of neurons which colonize the input domain, and “bridge”, a different kind of edge in the manifold graph, which signal the data nonstationarity. Some examples from artificial problems and a real application are given.
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This work has been partly supported by OPLON Italian MIUR project.
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Cirrincione, G., Randazzo, V., Pasero, E. (2018). Growing Curvilinear Component Analysis (GCCA) for Dimensionality Reduction of Nonstationary Data. In: Esposito, A., Faudez-Zanuy, M., Morabito, F., Pasero, E. (eds) Multidisciplinary Approaches to Neural Computing. Smart Innovation, Systems and Technologies, vol 69. Springer, Cham. https://doi.org/10.1007/978-3-319-56904-8_15
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DOI: https://doi.org/10.1007/978-3-319-56904-8_15
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