Piecewise linear projection based on self-organizing map

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review

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Original languageEnglish
Pages (from-to)151-163
Journal / PublicationNeural Processing Letters
Issue number2
Publication statusPublished - Oct 2002


A piecewise linear projection algorithm, based on Kohonen's Self-Organizing Map, is presented. Using this new algorithm, neural network is able to adapt its neural weights to accommodate with input space, while obtaining reduced 2-dimensional subspaces at each neural node. After completion of learning process, first project input data into their corresponding 2-D subspaces, then project all data in the 2-D subspaces into a reference 2-D subspace defined by a reference neural node. By piecewise linear projection, we can more easily deal with large data sets than other projection algorithms like Sammon's nonlinear mapping (NLM). There is no need to re-compute all the input data to interpolate new input data to the 2-D output space.

Research Area(s)

  • Dimension reduction, Piecewise linear projection, Sammon's nonlinear mapping, Self-organizing map