Optimal Randomized Canonical Correlation Analysis

Last update: Nov 21, 2021

Related tags

Machine Learning ORCCA

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:

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)

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.

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Optimal Randomized Canonical Correlation Analysis

Related tags

Overview

ORCCA

Some working exmaples for using ORCCA:

Owner

Yinsong Wang

Transpile trained scikit-learn estimators to C, Java, JavaScript and others.

Built various Machine Learning algorithms (Logistic Regression, Random Forest, KNN, Gradient Boosting and XGBoost. etc)

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This is a Machine Learning model which predicts the presence of Diabetes in Patients

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