Dimensionality reduction
The goal of dimensionality reduction is to create a small set of new variables that will describe the individuals in the data base nearly as well as do the original variables, which are usually quite numerous.
Lee, Michel Verleysen, Nonlinear Dimensionality Reduction, Springer, 2007.
^ A. N. Gorban, A. Zinovyev, Principal manifolds and graphs in practice: from molecular biology to dynamical systems, International Journal of Neural Systems, Vol.
Ways of dimensionality Reduction for Text
Latent Semantic Indexing
Locality Preserving Indexing
Probabilistic Latent Semantic Indexing ...
Dimensionality Reduction. Data Reduction by decreasing the dimensionality (exploratory multivariate statistics).
Although dimensionality reduction techniques have helped clarify how some of these ERPs interrelate, we still have basically no idea how these putatively different indices map to the cognitive processing of novelty, ...
Such clustering (and dimensionality reduction) is very useful as a preprocessing stage, whether for further neural network data processing, or for more traditional techniques.
Where are Neural Networks applicable?
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During training the network performs some kind of data compression such as dimensionality reduction or clustering.
This is only non-trivial if the hidden layer forms an information bottleneck - contains less units than the input (output) layer, so that the network must perform dimensionality reduction (a form of data compression).
 See also: Dimensionality, Classification, Neural network, Distribution, Regression
  
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