This repository provides code in R for the computer vision problem of human face recognition.
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Updated
Jul 1, 2020 - R
This repository provides code in R for the computer vision problem of human face recognition.
Method Principal Component Analysis
Supervised learning and unsupervised in R, with a focus on regression and classification methods.
Estadística Aplicada
Running through some R refresher
In this project, we use differents methods to transform our dataset (usually dimension modification) before making prediction thanks to machine learning and regressions.
This repository contains instructions to run the method, COGG or Correlation Optimization of Genetics and Geodemographics.
Figuring out which handwritten digits are most differentiated with PCA.
Classifying abstracts of different papers using unsupervised learning algorithms like soft and hard Expectation Maximization.
Project under the supervision of Prof. B. Krishna Mohan, Satellite Image Processing Lab, CSRE, IITB to denoise a 4 band satellite image using a pipeline of PCT, Removal of PC corresponding to lowest Eigen Value and Inverse PCT
Using principal component and clustering analysis on a customer segmentation case.
5 analytical tasks have been completed using VAT validated gower-PAM clustering, Correspondence Analysis (CA), Asym-Biplot, Multiple Correspondence Analysis (MCA), Chi-Squared test, Regression, and predictive classification models with KNN, SVM, and Random Forest.
Used Principal Component Analysis on Iris Dataset and reduced it from 4-features to 3-features and captured 93% of variance
Unsupervised Learning: Identify Customer Segments - Principal Component Analysis and Clustering
Faces recognition example using eigenfaces and SVMs
Real-time tool for exploring the relationships between PCA components and input features
This repository is a series of notebooks that show analysis and modeling of the Breast Cancer data from Kaggle.
DA incorporates the commonly used linear and non-linear, local and global supervised learning approaches (discriminant analysis). These discriminant analyses can be used to do ecological and evolutionary inference. We show the examples of demographic history inference, species identification, and population structure inference in the vignettes …
Minimal PCA library based on numpy and examples of practical dimensionality reduction use of the principal components in ETF market analysis.
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