https://u-picardie.hal.science/hal-03631447Pasero, ErosErosPaseroDET - Department of Electronics and Telecommunications [Torino] - Polito - Politecnico di Torino = Polytechnic of TurinCirrincione, GiansalvoGiansalvoCirrincioneLTI - Laboratoire des technologies innovantes - UR UPJV 3899 - UPJV - Université de Picardie Jules VerneRandazzo, VincenzoVincenzoRandazzoPolitechnico di TorinoGrowing Curvilinear Component Analysis (GCCA) for Dimensionality Reduction of Nonstationary DataHAL CCSD2018[SPI] Engineering Sciences [physics]DESSAIVRE, LouiseEsposito, A and FaudezZanuy, M and Morabito, FC and Pasero, E2022-04-05 16:25:182023-03-22 11:44:122022-04-05 16:25:18enBook sections10.1007/978-3-319-56904-8\_151Dealing 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.