Random Projection Algorithm: Difference between revisions

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A [[]] is a [[]] ...
A [[Random Projection Algorithm]] is a [[dimensionality compression algorithm]] ...
* <B>Counter-Example(s):</B>
* <B>Counter-Example(s):</B>
** [[Latent Dirichlet Allocation]].
** [[Latent Dirichlet Allocation]].
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<BR>
<BR>
* http://scikit-learn.org/stable/modules/random_projection.html
* http://scikit-learn.org/stable/modules/random_projection.html
** QUOTE: The [[sklearn.random_projection module]] implements a simple and computationally efficient way to reduce the dimensionality of the data by trading a controlled amount of accuracy (as additional variance) for faster processing times and smaller model sizes. This module implements two types of unstructured random matrix: Gaussian random matrix and sparse random matrix.  <P>  The dimensions and distribution of random projections matrices are controlled so as to preserve the pairwise distances between any two samples of the dataset. Thus random projection is a suitable approximation technique for distance based method.
** QUOTE: The [[sklearn.random_projection module]] implements a simple and computationally efficient way to reduce the dimensionality of the data by trading a controlled amount of accuracy (as additional variance) for faster processing times and smaller model sizes. This module implements two types of [[unstructured random matrix]]: [[Gaussian random matrix]] and [[sparse random matrix]].  <P>  The dimensions and distribution of random projections matrices are controlled so as to preserve the pairwise distances between any two samples of the dataset. Thus random projection is a suitable approximation technique for distance based method.


=== 2001 ===
=== 2001 ===

Revision as of 02:22, 3 February 2015

A Random Projection Algorithm is a dimensionality compression algorithm ...



References

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2001

2000

  • (Dasgupta, 2000) => Sanjoy Dasgupta. 2000. Experiments with random projection." In: Proceedings of the Sixteenth conference on Uncertainty in artificial intelligence (UAI‘00), Craig Boutilier and Moisés Goldszmidt (Eds.). Morgan Kaufmann Publishers Inc., San Francisco, CA, USA, 143-151.