jlt

Johnson-Lindenstrauss transform (JLT), random projection (RP), fast Johnson-Lindenstrauss transform (FJLT), and randomized Hadamard transform (RHT) in python 3.x

Supports linear mappings and radial basis function (RBF) mappings (a.k.a. Random Fourier Features) that reduce dimensionality while preserving the square of the $\ell_2$-norm between points with bounded error.

Created by: Ben Fauber, Dell Technologies, 02Apr2023

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Overview

Provides python 3.x functions based on the Johnson-Lindenstrauss (JL) lemma. The Johnson-Lindenstrauss Transform (JLT) preserves pair-wise distances with bounded error $\epsilon$ as points are projected from high-dimensional space $d$ into a lower-dimensional space $k$. The functions in this package accept $d$-dimensional vector and/or matrix/array inputs to return the $k$-dimensional output. The JLT preserves the square of the $\ell_2$-norm between points with bounded error $\epsilon$.

At a high level, the Johnson-Lindenstrauss transform (JLT) is a dimensionality-reduction technique as illustrated below, where $n > 0$ and typically $d >> k$.

Specifically, a Johnson-Lindenstrauss transform (JLT) $\Phi$ is a random linear map for any set $Z$ of $n$-points in $d$-dimensions, defined by a matrix $B \in \mathbb{R}^{k \times d}$, where $\epsilon \in (0,1]$ and the pair-wise Euclidean distance between points $u$ and $v$, $\forall (u,v) \in Z$, is defined by $(1 - \epsilon)||u-v||^2_{\ell_2} \le ||Bu-Bv||^2_{\ell_2} \le (1 + \epsilon)||u-v||^2_{\ell_2}$.

The above equation can be further simplified where $B$ is replaced with the JLT linear map $\Phi$ and $x = u - v$ such that $||\Phi x||^2_{\ell_2} = (1 \pm \epsilon)||x||^2_{\ell_2} \quad \forall x \in Z$.

The figures below illustrate: 1) JLT algorithm runtimes; and 2) preservation of the square of the $\ell_2$-norm by the Fast JLT (FJLT). Random projections (RP) and Fast Johnson-Lindenstrauss Transform (FJLT) are faster versions of the original JLT, and subsampled randomized Hadamard transforms (SRHT) are even faster yet (first figure, gold line). The FJLT preserves the square of the $\ell_2$-norm regardless of the sparsity of the input (second figure). In both of the figures, $d$ is held constant at $d$ = 16,384 and $k$ is varied (x-axis).

JLT has applications in linear mappings, random projections, locality-sensitive hashing LSH, matrix sketching, low-rank matrix approximations, and sparse recovery.

For more info, check out the survey article, "An Introduction to Johnson–Lindenstrauss Transforms" (2021) by Casper Benjamin Freksen for a nice overview of JLT methods, their variations, and applications.

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Dependencies and Installing

Dependencies

Python 3.x packages math, numpy, scipy.sparse, and fht (https://github.com/nbarbey/fht)

Installing

1. Clone the linearMapping.py python file to your working directory using either:

• Python command line

git clone https://github.com/dell/jlt.git

or

• Jupyter Notebook

import os, sys

path = os.getcwd()
os.chdir(path)

!git clone https://github.com/dell/jlt.git

sys.path.insert(0, path+'\jlt')

2. Import the module into your script:

[in]> from linearMapping import linearMapping, rbfMapping

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Functions

linearMapping()

Produces linear mapping of input vector or array from d dimensions into k dimensions, typically applied where $d >> k$. Provides bounded guarantees of Johnson-Lindenstrauss lemma when k is determined automatically (i.e., k=None), via the method of Dasgupta and Gupta, with user-defined eps ($\epsilon$ in Johnson-Lindenstrauss lemma) as the error associated with the preservation of the $\ell_2$-norm.

[in]> linearMapping(A, k=None, eps=0.1, method='FJLT', p=2, random_seed=21)
[out]> # d-to-k linear mapping of A

A is the input vector $A \in \mathbb{R}^{d}$ or matrix $A \in \mathbb{R}^{n \times d}$.

method accepts one of several variants of the JLT: JLT, SparseRP, VerySparseRP, FJLT, or SRHT. See References section below for more details on each method.

p is the $\ell{p}$-norm where $p \in \{ 1, 2 \}$ and is only relevant to the FJLT method.

random_seed is the random seed value for the generator function that randomizes the Gaussian and/or the row-selector function, based on the method employed.

Defaults are: k=None, eps=0.1, method=FJLT, p=2, and random_seed=21. Code is fully commented -- variables and helper functions are further defined within the PY file.

The user can further edit the code to specify sampling with replacement swr or sampling without replacement swor for either faster or more accurate calculations, respectively. NOTE: swor is recommended when solving for inverse matrices with iterative solvers (e.g., compressed sensing applications).

rbfMapping()

Produces radial basis function (RBF) mapping (a.k.a. Random Fourier Features) of input vector or array from d dimensions into k dimensions, typically applied where $d >> k$. Provides bounded guarantees of Johnson-Lindenstrauss lemma when k is determined automatically (i.e., k=None), via the method of Dasgupta and Gupta, with user-defined eps ($\epsilon$ in Johnson-Lindenstrauss lemma) as the error associated with the preservation of the $\ell_2$-norm.

[in]> rbfMapping(A, k=None, method='SRHT-RFF', gamma=1.0, random_seed=21)
[out]> # d-to-k radial basis function mapping of A

A is the input vector $A \in \mathbb{R}^{d}$ or matrix $A \in \mathbb{R}^{n \times d}$.

method accepts two variants of the RBF: RFF or SRHT-RFF. See References section below for more details on each method.

gamma is the standard deviation of the Gaussian distribution.

random_seed is the random seed value for the generator function that randomizes the Gaussian and/or the row-selector function, based on the method employed.

Defaults are: k=None, method=SRHT-RFF, gamma=1.0, and random_seed=21. Code is fully commented -- variables and helper functions are further defined within the PY file.

The user can further edit the code to specify sampling with replacement swr or sampling without replacement swor for either faster or more accurate calculations, respectively. NOTE: swor is recommended when solving for inverse matrices with iterative solvers (e.g., compressed sensing applications).

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References

JLT W. B. Johnson and J. Lindenstrauss, "Extensions of Lipschitz mappings into a Hilbert Space." Contemp. Math. 1984, 26, 189-206. link to paper

SparseRP Dimitris Achlioptas, "Database-friendly random projections: Johnson-Lindenstrauss with binary coins." J. Comput. Syst. Sci. 2003, 66(4), 671-687. link to paper

VerySparseRP L. Peng, T. J. Hastie, K. W. Church, "Very sparse random projections." KDD 2006, Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining, August 2006, pages 287–296. link to paper

FJLT N. Ailon and B. Chazelle, "Approximate Nearest Neighbors and the Fast Johnson-Lindenstrauss Transform." STOC’06, May21–23, 2006, Seattle, Washington, USA. link to paper

SRHT F. Krahmer and R. Ward, "New and improved Johnson-Lindenstrauss embeddings via the restricted isometry property." SIAM J. Math. Anal. 2011, 43(3), 1269–1281. link to paper

SRHT N. Ailon and E. Liberty, "Almost Optimal Unrestricted Fast Johnson-Lindenstrauss Transform." ACM Trans. Algorithms 2013, 9(3), 1–12. link to paper

RFF A. Rahimi and B. Recht. "Random Features for Large-Scale Kernel Machines." NeurIPS 2007. link to paper

SRHT-RFF Y. Cherapanamjeri and J. Nelson. "Uniform Approximations for Randomized Hadamard Transforms with Applications." 2022 Proceedings of the 54th Annual ACM SIGACT Symposium on Theory of Computing (STOC), 659–671. link to paper

k S. Dasgupta and A. Gupta. "An elementary proof of the Johnson-Lindenstrauss Lemma." 1999. link to paper

Tight lower bounds for k K. G. Larsen and J. Nelson. "Optimality of the Johnson-Lindenstrauss Lemma." 2017 IEEE 58th Annual Symposium on Foundations of Computer Science (FOCS). link to paper

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Citing this Repo

@misc{FauberJLT2023,
  author = {Fauber, B. P.},
  title = {Johnson-Lindenstrauss Transforms},
  year = {2023},
  publisher = {GitHub},
  journal = {GitHub repository},
  howpublished = {\url{https://github.com/dell/jlt}}
}