Curvilinear Component Analysis : A Self-Organizing Neural Network for Nonlinear Mapping of Data Sets  

Oleh:

Herault, J. ; Demartines, P.

Jenis: Article from Journal - ilmiah internasional
Dalam koleksi: IEEE Transactions on Neural Networks vol. 8 no. 1 (1997), page 148-154.
Topik: ANALYSIS; curvilinear; component analysis; self - organizing; neural network; non linear; mapping; data sets

Ketersediaan

Perpustakaan Pusat (Semanggi) Nomor Panggil: II36.2 Non-tandon: 1 (dapat dipinjam: 0) Tandon: tidak ada

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Isi artikel

We present a new strategy called “curvilinear component analysis” (CCA) for dimensionality reduction and representation of multidimensional data sets. The principle of CCA is a self - organized neural network performing two tasks : vector quantization (VQ) of the submanifold in the data set (input space); and non linear projection (P) of these quantizing vectors toward an output space, providing a revealing unfolding of the submanifold. After learning, the network has the ability to continuously map any new point from one space into another: forward mapping of new points in the input space, or backward mapping of an arbitrary position in the output space.

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