Random Projection Algorithm
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A Random Projection Algorithm is a dimensionality compression algorithm ...
- Counter-Example(s):
- See: Semantic Analysis Task, Singular-Value Decomposition.
References
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.
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.