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]]. | ||
** [[Latent Semantic Analysis]]. | ** [[Latent Semantic Analysis]]. | ||
** [[Reflective Random Indexing]]. | ** [[Reflective Random Indexing]]. | ||
** [[Random Indexing Algorithm]]. | |||
* <B>See:</B> [[Semantic Analysis Task]], [[Singular-Value Decomposition]]. | * <B>See:</B> [[Semantic Analysis Task]], [[Singular-Value Decomposition]]. | ||
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==References == | |||
== References == | |||
=== 2016 === | |||
* ([[Wikipedia, 2015]]) ⇒ http://wikipedia.org/wiki/Random_projection , Retrived:2016-3-5 | |||
** QUOTE: In mathematics and statistics, '''random projection''' is a technique used to [[dimensionality reduction|reduce the dimensionality]] of a set of points which lie in [[Euclidean space]]. Random projection methods are powerful methods known for their simplicity and less erroneous output compared with other methods. According to experimental results, random projection preserve distances well, but empirical results are sparse. | |||
=== 2014 === | === 2014 === | ||
* https://code.google.com/p/semanticvectors/ | * https://code.google.com/p/semanticvectors/ | ||
** QUOTE: | ** QUOTE: … The models are created by applying [[concept mapping algorithm]]s to [[term-document matric]]es created using [[Apache Lucene]]. The [[concept mapping algorithm]]s supported by the package include [[Random Projection Algorithm|Random Projection]], [[Latent Semantic Analysis (LSA)]] and [[Reflective Random Indexing]]. ... | ||
<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. | ** 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. | ||
=== 2011 === | |||
* ([[2011_CHIRPANewClassifierbasedonCompo|Wilkinson et al., 2011]]) ⇒ [[Leland Wilkinson]], [[Anushka Anand]], and [[Dang Nhon Tuan]]. ([[2011]]). “CHIRP: A New Classifier based on Composite Hypercubes on Iterated Random Projections.” In: [[Proceedings of the 17th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining]] ([[KDD-2011]]) Journal. ISBN:978-1-4503-0813-7 [http://dx.doi.org/10.1145/2020408.20 | |||
=== 2001 === | === 2001 === | ||
* ([[Bingham & Mannila, 2001]]) | * ([[Bingham & Mannila, 2001]]) ⇒ [[Ella Bingham]] and [[Heikki Mannila]]. ([[2001]]). “Random Projection in Dimensionality Reduction: Applications to image and text data.” In: Proceedings of the seventh ACM SIGKDD International Conference on Knowledge Discovery and Data Mining ([[KDD-2001]]). | ||
=== 2000 === | === 2000 === | ||
* ([[Dasgupta, 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. | ||
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__NOTOC__ | __NOTOC__ | ||
[[Category:Concept]] | |||
Latest revision as of 06:55, 7 January 2023
A Random Projection Algorithm is a dimensionality compression algorithm ...
- …
- Counter-Example(s):
- See: Semantic Analysis Task, Singular-Value Decomposition.
References
2016
- (Wikipedia, 2015) ⇒ http://wikipedia.org/wiki/Random_projection , Retrived:2016-3-5
- QUOTE: In mathematics and statistics, random projection is a technique used to reduce the dimensionality of a set of points which lie in Euclidean space. Random projection methods are powerful methods known for their simplicity and less erroneous output compared with other methods. According to experimental results, random projection preserve distances well, but empirical results are sparse.
2014
- https://code.google.com/p/semanticvectors/
- QUOTE: … The models are created by applying concept mapping algorithms to term-document matrices created using Apache Lucene. The concept mapping algorithms supported by the package include Random Projection, Latent Semantic Analysis (LSA) and Reflective Random Indexing. ...
- 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.
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.
2011
- (Wilkinson et al., 2011) ⇒ Leland Wilkinson, Anushka Anand, and Dang Nhon Tuan. (2011). “CHIRP: A New Classifier based on Composite Hypercubes on Iterated Random Projections.” In: Proceedings of the 17th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD-2011) Journal. ISBN:978-1-4503-0813-7 [http://dx.doi.org/10.1145/2020408.20
2001
- (Bingham & Mannila, 2001) ⇒ Ella Bingham and Heikki Mannila. (2001). “Random Projection in Dimensionality Reduction: Applications to image and text data.” In: Proceedings of the seventh ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD-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.