random-projections
There are 21 repositories under random-projections topic.
james-bowman/nlp
Selected Machine Learning algorithms for natural language processing and semantic analysis in Golang
cmdevries/LMW-tree
Learning M-Way Tree - Web Scale Clustering - EM-tree, K-tree, k-means, TSVQ, repeated k-means, bitwise clustering
dselivanov/LSHR
Locality Sensitive Hashing In R
djpasseyjr/Shrike.jl
Shrike.jl: Fast approximate nearest neighbor search with random projection trees. (Benchmarks included)
IBM/pylspack
A lightweight, multithreaded Python package for sketching, column selection, leverage scores and related computations.
dell/jlt
Johnson-Lindenstrauss transform (JLT), random projections (RP), fast Johnson-Lindenstrauss transform (FJLT), and randomized Hadamard transform (RHT) in python 3.x
zgornel/StringAnalysis.jl
Hard-Forked from JuliaText/TextAnalysis.jl
popcornell/tfelm
An experimental API for Extreme Learning machines Neural Networks made with TensorFlow.
egarpor/rp.flm.test
Software companion for "Goodness-of-fit tests for the functional linear model based on randomly projected empirical processes"
ocramz/rp-tree
Random projection trees
ginevracoal/adversarial_examples
Random Projections for improved Adversarial Robustness
krish1919ls/machine_learning_models
Assignments from Applied Machine Learning Class (UTD BUAN-6341)
akash13singh/name_matching
Name matching using Approximate Nearest Neigbours
diegovalsesia/randomized-local-aggregations
Code for Sampling of Graph Signals via Randomized Local Aggregations
kcin96/RandomProjections.jl
This package is a Julia implementation of random projections.
praveena2j/Traitor-Tracing-System-Based-on-Random-Projections
A Code and Domain Independent Traitor Tracing System Based on Random Projections
techbrainwave/Unsupervised-Learning
Application of unsupervised learning and dimensionality reduction towards multiple problem sets.
tomessilva/fort
fast orthogonal random transforms (in R)
anirban-code-to-live/tipr-first-assignment
Hola, amigos! Welcome to the first assignment of TIPR-2019.
egarpor/data-gofflm
Data for the reproducibility of Cuesta-Albertos et al. (2019)
Emrys-Hong/UROPs
font-recommender