137 Open source projects that are alternatives of or similar to moses

Federated Principal Component Analysis Revisited!

👑 Python factor analysis library (PCA, CA, MCA, MFA, FAMD)

A repository for recording the machine learning code

In this repository, You can find the files which implement dimensionality reduction on the hyperspectral image(Indian Pines) with classification.

AiLearning: 机器学习 - MachineLearning - ML、深度学习 - DeepLearning - DL、自然语言处理 NLP

Randomized Dimension Reduction Library

H2Oai GPU Edition

Simple Implementation of Network Intrusion Detection System. KddCup'99 Data set is used for this project. kdd_cup_10_percent is used for training test. correct set is used for test. PCA is used for dimension reduction. SVM and KNN supervised algorithms are the classification algorithms of project. Accuracy : %83.5 For SVM , %80 For KNN

PCA that iteratively replaces missing data

Numerical analysis course in Python

Code that might be useful to others for learning/demonstration purposes, specifically along the lines of modeling and various algorithms. Now almost entirely superseded by the models-by-example repo.

Efficient similarity search and clustering for Ruby

Mathematical & Statistical topics to perform statistical analysis and tests; Linear Regression, Probability Theory, Monte Carlo Simulation, Statistical Sampling, Bootstrapping, Dimensionality reduction techniques (PCA, FA, CCA), Imputation techniques, Statistical Tests (Kolmogorov Smirnov), Robust Estimators (FastMCD) and more in Python and R.

A Python library for adversarial machine learning focusing on benchmarking adversarial robustness.

Comparison of dimensionality reduction methods

Matlab implementation of multi-view low-rank sparse subspace clustering

HoG, PCA, PSO, Hard Negative Mining, Sliding Window, Edge Boxes, NMS

Solutions to labs and excercises from An Introduction to Statistical Learning, as Jupyter Notebooks.

Explore high-dimensional datasets and how your algo handles specific regions.

t-digest module for Redis

A Python Library for Similarity-based Dimensionality Reduction

tf-recsys contains collaborative filtering (CF) model based on famous SVD and SVD++ algorithm. Both of them are implemented by tensorflow in order to utilize GPU acceleration.

Code for "Effective Dimensionality Reduction for Word Embeddings".

PCA and DBSCAN based anomaly and outlier detection method for time series data.

Easy genetic ancestry predictions in Python

A collection of Poisson lognormal models for multivariate count data analysis

Maximum Covariance Analysis in Python

OnLine Low-rank Subspace tracking by TEnsor CP Decomposition in Matlab: Version 1.0.1

Implement face recognition using PCA, LDA and LPP

A repository of resources for understanding the concepts of machine learning/deep learning.

📊 Computation and processing of models' parameters

Sequential Tree Sampler for online phylogenetics

Starter code of Prof. Andrew Ng's machine learning MOOC in R statistical language

No description or website provided.

Workshop (6 hours): preprocessing, cross-validation, lasso, decision trees, random forest, xgboost, superlearner ensembles

A collection of Poisson lognormal models for multivariate count data analysis

🏗 Statistical models for biomolecular dynamics 🏗

Animated Visualizations of Popular Machine Learning Algorithms

Project Page of Combining 3D Morphable Models: A Large scale Face-and-Head Model - [CVPR 2019]

Source code for the 2019 IEEE TIFS paper "PalmNet: Gabor-PCA Convolutional Networks for Touchless Palmprint Recognition"

Uncertainty-aware principal component analysis.

R Interface to the Spectra Library for Large Scale Eigenvalue and SVD Problems

No description or website provided.

H2O is an Open Source, Distributed, Fast & Scalable Machine Learning Platform: Deep Learning, Gradient Boosting (GBM) & XGBoost, Random Forest, Generalized Linear Modeling (GLM with Elastic Net), K-Means, PCA, Generalized Additive Models (GAM), RuleFit, Support Vector Machine (SVM), Stacked Ensembles, Automatic Machine Learning (AutoML), etc.

Feature reduction projections and classifier models are learned by training dataset and applied to classify testing dataset. A few approaches of feature reduction have been compared in this paper: principle component analysis (PCA), linear discriminant analysis (LDA) and their kernel methods (KPCA,KLDA). Correspondingly, a few approaches of classification algorithm are implemented: Support Vector Machine (SVM), Gaussian Quadratic Maximum Likelihood and K-nearest neighbors (KNN) and Gaussian Mixture Model(GMM).

Workshop on tidytranscriptomics: Performing tidy transcriptomics analyses with tidybulk, tidyverse and tidyheatmap

OCR Machine Learning in python

SJTU CS386 Digital Image Processing

machine learning algorithms in Swift

A tidyverse suite for (pre-) machine-learning: cluster, PCA, permute, impute, rotate, redundancy, triangular, smart-subset, abundant and variable features.

A lean C++ library for working with point cloud data

Playing with MNIST. Machine Learning. Generative Models.

Imputation method for scRNA-seq based on low-rank approximation

Machine Learning Lectures at the European Space Agency (ESA) in 2018

In This repository I made some simple to complex methods in machine learning. Here I try to build template style code.

Python code for common Machine Learning Algorithms

🕝 Time-warped principal components analysis (twPCA)

If you can measure it, consider it predicted

A MATLAB toolbox for classifier: Version 1.0.7

Quick-Data-Science-Experiments