Results 211 to 220 of about 1,974,250 (242)

LSTD with Random Projections.

2010
We consider the problem of reinforcement learning in high-dimensional spaces when the number of features is bigger than the number of samples. In particular, we study the least-squares temporal difference (LSTD) learning algorithm when a space of low dimension is generated with a random projection from a high-dimensional space.
Ghavamzadeh, Mohammad, Lazaric, Alessandro, Maillard, Odalric, Munos, Rémi   +3 more
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Random Projections for SVM Ensembles

2010
Data projections have been used extensively to reduce input space dimensionality. Such reduction is useful to get faster results, and sometimes can help to discard unnecessary or noisy input dimensions. Random Projections (RP) can be computed faster than other methods as for example Principal Component Analysis (PCA).
Jesús Maudes, Juan José Rodríguez, César Ignacio García-Osorio, Carlos Pardo   +3 more
openaire   +1 more source

Very sparse random projections

Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining, 2006
There has been considerable interest in random projections, an approximate algorithm for estimating distances between pairs of points in a high-dimensional vector space. Let A in Rn x D be our n points in D dimensions. The method multiplies A by a random matrix R in RD x k, reducing the D dimensions down to just k for speeding up the computation.
Ping Li 0001, Trevor Hastie, Kenneth Ward Church   +2 more
openaire   +1 more source

Random Projection Ensemble Classifiers

2009
We introduce a novel ensemble model based on random projections. The contribution of using random projections is two-fold. First, the randomness provides the diversity which is required for the construction of an ensemble model. Second, random projections embed the original set into a space of lower dimension while preserving the dataset’s geometrical ...
Alon Schclar, Lior Rokach
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Random Projections with Bayesian Priors

2018
The technique of random projection is one of dimension reduction, where high dimensional vectors in \(\mathbb R^D\) are projected down to a smaller subspace in \(\mathbb R^k\). Certain forms of distances or distance kernels such as Euclidean distances, inner products [10], and \(l_p\) distances [12] between high dimensional vectors are approximately ...
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Classification with Sign Random Projections

2014
Sign random projections (SRP) transform multi-dimensional vector into a binary string storing only the sign of the random projection values. Previous works showed that the obtained binary strings can be used to estimate the angle between vectors which can be used to speed up the nearest neighbors search.
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Beta Random Projection

Ninth IEEE International Symposium on Multimedia Workshops (ISMW 2007), 2007
Yu-En Lu, Pietro Liò, Steven Hand 0001
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Database-friendly random projections

Proceedings of the twentieth ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems, 2001
A classic result of Johnson and Lindenstrauss asserts that any set of n points in d-dimensional Euclidean space can be embedded into k-dimensional Euclidean space where k is logarithmic in n and independent of d so that all pairwise distances are maintained within an arbitrarily small factor.
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Random subspace and random projection nearest neighbor ensembles for high dimensional data

Expert Systems With Applications, 2022
Henrik Boström, Panagiotis Papapetrou, Sampath Deegalla   +2 more
exaly  

Random Projections

2021
B. K. Tripathy, S Anveshrithaa, Shrusti Ghela   +2 more
openaire   +1 more source