Dimensionality Reduction Algorithm

From GM-RKB
Jump to: navigation, search

A Dimensionality Reduction Algorithm is a data transformation algorithm that can be implemented into a dimensionality reduction system (that can solve a dimensionality reduction task).



References

2011

2002

  • (Fodor, 2002) ⇒ Imola K. Fodor. (2002). “A Survey of Dimension Reduction Techniques." LLNL technical report, UCRL ID-148494
    • QUOTE: ... We distinguish two major types of dimension reduction methods: linear and non-linear.

      ... Traditional statistical methods break down partly because of the increase in the number of observations, but mostly because of the increase in the number of variables associated with each observation. The dimension of the data is the number of variables that are measured on each observation.

      High-dimensional datasets present many mathematical challenges as well as some opportunities, and are bound to give rise to new theoretical developments [11]. One of the problems with high-dimensional datasets is that, in many cases, not all the measured variables are "important" for understanding the underlying phenomena of interest. While certain computationally expensive novel methods [4] can construct predictive models with high accuracy from high-dimensional data, it is still of interest in many applications to reduce the dimension of the original data prior to any modeling of the data.