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Search results for matrix pca
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47 search results found
Ml
⭐
2,235
Machine learning tools in JavaScript
Hyperlearn
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1,387
2-2000x faster ML algos, 50% less memory usage, works on all hardware - new and old.
Lrslibrary
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741
Low-Rank and Sparse Tools for Background Modeling and Subtraction in Videos
Mathematics
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386
数学知识点滴积累 矩阵 数值优化 神经网络反向传播 图优化 概率论 随机过程 卡尔曼滤波 粒子滤波 数学函数拟合
Ccontrol
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190
Using advanced control techniques in an easy way for embedded - No theory, only practice
Datagene
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170
DataGene - Identify How Similar TS Datasets Are to One Another (by @firmai)
Asap
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113
ASAP is a package that can quickly analyze and visualize datasets of crystal or molecular structures.
Robustpca
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109
Robust PCA implementation and examples (Matlab)
Irlba
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106
Fast truncated singular value decompositions
Ristretto
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94
Randomized Dimension Reduction Library
Druidjs
⭐
87
A JavaScript Library for Dimensionality Reduction
Rsvd
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76
Randomized Matrix Decompositions using R
Local_pca
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59
Methods for examining PCA locally along the genome.
Pca_transform
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44
Java PCA transformation of a data matrix
Nmfadmm
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39
A sparsity aware implementation of "Alternating Direction Method of Multipliers for Non-Negative Matrix Factorization with the Beta-Divergence" (ICASSP 2014).
Pcaworkshop
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35
An introduction to matrix factorization and PCA and SVD.
Spca
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33
Sparse Principal Component Analysis (SPCA) using Variable Projection
Logisticpca
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33
Dimensionality reduction for binary data
Combining3dmorphablemodels
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29
Project Page of Combining 3D Morphable Models: A Large scale Face-and-Head Model - [CVPR 2019]
Hpca
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29
C++ implementation of the Hellinger PCA for computing word embeddings.
Nanny
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27
A tidyverse suite for (pre-) machine-learning: cluster, PCA, permute, impute, rotate, redundancy, triangular, smart-subset, abundant and variable features.
Principal Components Analysis
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27
Python/Numpy PCA using the transpose trick.
Frequent Direction
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26
Implementation of Frequent-Directions algorithm for efficient matrix sketching [E. Liberty, SIGKDD2013]
Fscnmf
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16
An implementation of "Fusing Structure and Content via Non-negative Matrix Factorization for Embedding Information Networks".
Generalizedpca
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15
Dimensionality reduction for exponential family data by extending PCA
Randomizedsvd
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15
C++/Eigen implementation of fast randomized SVD
Simclda
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13
Prediction of lncRNA-disease associations based on inductive matrix completion
Pbdml
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13
Bksvd
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13
Fast randomized block Krylov method for the singular value decomposition
Luigi_gdb_pipeline_demo
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11
An example to illustrate using Luigi to manage a data science workflow in Greenplum Database
Surface Registration Tool
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11
This software register two surface meshes by iterative closest point method.
Epca
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10
Exploratory Principal Component Analysis
Cuda Pca Jacobi
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9
CUDA C implementation of Principal Component Analysis (PCA) through Singular Value Decomposition (SVD) using a highly parallelisable version of the Jacobi eigenvalue algorithm.
Mml Feature Learning
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8
Miami Machine Learning Meetup - Feature Learning with Matrix Factorization and Neural Networks
Petal Decomposition
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8
Matrix decomposition algorithms including PCA (principal component analysis) and ICA (independent component analysis)
Matrixproductpca
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8
Code for our paper "Single Pass PCA of Matrix Products"
Pca
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8
Implementation for Principal Component Analysis algorithm
Covfactormodel
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8
Covariance Matrix Estimation via Factor Models
Principal Component Analysis
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7
Project for exploring PCA through a simple Java implementation.
Py Matrix Algorithms
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6
Python library of 2-dimensional matrix algorithms.
Robustpca
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6
Robust Orthonormal Subspace Learning in Python
Sparseeigen
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6
Computation of Sparse Eigenvectors of a Matrix
Fast_pca
⭐
5
Fast & memory efficient Principal Components Analysis
Pca High Dim Cpp
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5
Principle Component Analysis designed specifically for High Dimensional cases.
Ulapack
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5
Micro Linear Algebra Package
Cuda Rpca Admm
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5
A CUDA implementation of performing Robust PCA for foreground-background separation, using ADMM for optimization.
Parallel Pca Openmp
⭐
5
A parallelized implementation of Principal Component Analysis (PCA) using Singular Value Decomposition (SVD) in OpenMP for C. The procedure used is Modified Gram Schmidt algorithm. The method for Classical Gram Schmidt is also available for use.
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