Optimal Randomized Canonical Correlation Analysis

Related tags

Machine LearningORCCA
Overview

ORCCA

Optimal Randomized Canonical Correlation Analysis

This project is for the python version of ORCCA algorithm.

It depends on Numpy for matrix calculation and works with any CCA calculation package. Here we recommend

cca zoo https://github.com/jameschapman19/cca_zoo

$ pip install cca-zoo

for CCA calculation as it provides several other CCA algorithms that can be used in algorithm comparison. Please feel free to delete the cca_zoo dependency in the manuscript by deleting line2 and ORCCA_cor function then use another CCA package of your choice.

Some working exmaples for using ORCCA:

  1. Generate ORCCA mapping for a given pair of dataset X and Y with 5 reselected random features

sample = ORCCA(X,Y,width1=0.1)

sample.ORCCA_mapping(m=5)

  1. Calculate the canonical correlations for a given pair of dataset X and Y with 5 reselected random features

sample = ORCCA(X,Y,width1=0.1)

sample.ORCCA_cor(m=5)

Owner
Yinsong Wang
I am a Ph.D. student at Northeastern University advised by Prof. Shahin Shahrampour. My research interest lies in general machine learning.
Yinsong Wang
scikit-fem is a lightweight Python 3.7+ library for performing finite element assembly.

scikit-fem is a lightweight Python 3.7+ library for performing finite element assembly. Its main purpose is the transformation of bilinear forms into sparse matrices and linear forms into vectors.

Tom Gustafsson 297 Dec 13, 2022
Model Agnostic Confidence Estimator (MACEST) - A Python library for calibrating Machine Learning models' confidence scores

Model Agnostic Confidence Estimator (MACEST) - A Python library for calibrating Machine Learning models' confidence scores

Oracle 95 Dec 28, 2022
We have a dataset of user performances. The project is to develop a machine learning model that will predict the salaries of baseball players.

Salary-Prediction-with-Machine-Learning 1. Business Problem Can a machine learning project be implemented to estimate the salaries of baseball players

Ayşe Nur Türkaslan 9 Oct 14, 2022
nn-Meter is a novel and efficient system to accurately predict the inference latency of DNN models on diverse edge devices

A DNN inference latency prediction toolkit for accurately modeling and predicting the latency on diverse edge devices.

Microsoft 241 Dec 26, 2022
Tools for diffing and merging of Jupyter notebooks.

nbdime provides tools for diffing and merging of Jupyter Notebooks.

Project Jupyter 2.3k Jan 03, 2023
Price forecasting of SGB and IRFC Bonds and comparing there returns

Project_Bonds Project Title : Price forecasting of SGB and IRFC Bonds and comparing there returns. Introduction of the Project The 2008-09 global fina

Tishya S 1 Oct 28, 2021
Python based GBDT implementation

Py-boost: a research tool for exploring GBDTs Modern gradient boosting toolkits are very complex and are written in low-level programming languages. A

Sberbank AI Lab 20 Sep 21, 2022
A Python-based application demonstrating various search algorithms, namely Depth-First Search (DFS), Breadth-First Search (BFS), and A* Search (Manhattan Distance Heuristic)

A Python-based application demonstrating various search algorithms, namely Depth-First Search (DFS), Breadth-First Search (BFS), and the A* Search (using the Manhattan Distance Heuristic)

17 Aug 14, 2022
Skoot is a lightweight python library of machine learning transformer classes that interact with scikit-learn and pandas.

Skoot is a lightweight python library of machine learning transformer classes that interact with scikit-learn and pandas. Its objective is to ex

Taylor G Smith 54 Aug 20, 2022
This repo includes some graph-based CTR prediction models and other representative baselines.

Graph-based CTR prediction This is a repository designed for graph-based CTR prediction methods, it includes our graph-based CTR prediction methods: F

Big Data and Multi-modal Computing Group, CRIPAC 47 Dec 30, 2022
This is a curated list of medical data for machine learning

Medical Data for Machine Learning This is a curated list of medical data for machine learning. This list is provided for informational purposes only,

Andrew L. Beam 5.4k Dec 26, 2022
Cool Python features for machine learning that I used to be too afraid to use. Will be updated as I have more time / learn more.

python-is-cool A gentle guide to the Python features that I didn't know existed or was too afraid to use. This will be updated as I learn more and bec

Chip Huyen 3.3k Jan 05, 2023
A toolbox to iNNvestigate neural networks' predictions!

iNNvestigate neural networks! Table of contents Introduction Installation Usage and Examples More documentation Contributing Releases Introduction In

Maximilian Alber 1.1k Jan 05, 2023
cleanlab is the data-centric ML ops package for machine learning with noisy labels.

cleanlab is the data-centric ML ops package for machine learning with noisy labels. cleanlab cleans labels and supports finding, quantifying, and lear

Cleanlab 51 Nov 28, 2022
Sequence learning toolkit for Python

seqlearn seqlearn is a sequence classification toolkit for Python. It is designed to extend scikit-learn and offer as similar as possible an API. Comp

Lars 653 Dec 27, 2022
A toolkit for geo ML data processing and model evaluation (fork of solaris)

An open source ML toolkit for overhead imagery. This is a beta version of lunular which may continue to develop. Please report any bugs through issues

Ryan Avery 4 Nov 04, 2021
100 Days of Machine and Deep Learning Code

💯 Days of Machine Learning and Deep Learning Code MACHINE LEARNING TOPICS COVERED - FROM SCRATCH Linear Regression Logistic Regression K Means Cluste

Tanishq Gautam 66 Nov 02, 2022
Formulae is a Python library that implements Wilkinson's formulas for mixed-effects models.

formulae formulae is a Python library that implements Wilkinson's formulas for mixed-effects models. The main difference with other implementations li

34 Dec 21, 2022
Apple-voice-recognition - Machine Learning

Apple-voice-recognition Machine Learning How does Siri work? Siri is based on large-scale Machine Learning systems that employ many aspects of data sc

Harshith VH 1 Oct 22, 2021
Continuously evaluated, functional, incremental, time-series forecasting

timemachines Autonomous, univariate, k-step ahead time-series forecasting functions assigned Elo ratings You can: Use some of the functionality of a s

Peter Cotton 343 Jan 04, 2023