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Margin-constrained Random Projections And Very Sparse Random Projections
Random Projections Sampling Maximum Likelihood Asymptotic Analysis
2015/8/21
We propose methods for improving both the accuracy and efficiency of random projections, the popular dimension reduction technique in machine learning and data mining, particularly useful for estimati...
Improving Random Projections Using Marginal Information
Random Projections Marginal Information
2015/8/21
We present an improved version of random projections that takes advantage of marginal norms. Using a maximum likelihood estimator (MLE), marginconstrained random projections can improve estimation acc...
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 ∈ Rn×D be our n points i...
A Unified Near-Optimal Estimator For Dimension Reduction in lα (0 < α ≤ 2) Using Stable Random Projections
Near-Optimal Estimator Dimension Reduction Stable Random Projections
2015/8/21
Many tasks (e.g., clustering) in machine learning only require the lα distances instead of the original data. For dimension reductions in the lα norm (0 < α ≤ 2), the method of stable random projectio...
Nonlinear Estimators and Tail Bounds for Dimension Reduction in l1 Using Cauchy Random Projections
dimension reduction l1 norm Johnson-Lindenstrauss (JL) lemma Cauchy random projections
2015/8/21
For1 dimension reduction in the l1 norm, the method of Cauchy random projections multiplies the original data matrix A ∈ Rn×D with a random matrix R ∈ RD×k (k D) whose entries are i.i.d. samples of ...