l1-pcahp's Introduction
This repository contains all the code used for the "Computational Results" of the paper "Robust Principal Component Analysis by Reverse Iterative Linear Programming" Andrea Visentin, Steven Prestwich and Armagan Tarim ## FILE LIST ## ./data: ## this directory contains all the dataset. You must ## retrive them ## from http://archive.ics.uci.edu/ml/index.html ./pca: ## this directory contains the implementation of PCA l1pcahp.R ## the solution we introduce in the paper ## more detailed description at the end of the page ## implementation op PCA in R l2pca.R ## a container function ./src: ## this directory contains shared functions classifier.R ## 10cv 1nn classification used in Section 4.3 dataset_manager.R ## load, clean and format the data for Sections 4.2, ## 4.3 and 4.5 syntetic_data_generator.R ## data generator for test in Section 4.1 ./test: classification.R ## test present in Section 4.3 initialization_comparation.R ## test present in Section 4.5 outliers_reconstruction.R ## test present in Section 4.2, reconstruction error outliers_variance.R ## test present in Section 4.5, variance of the projected ## points syntetic.R ## test present in Section 4.1 time_comparison.R ## test present in Section 4.4 ## R PACKAGE REQUIRED ## -pcaL1 -pcaPP -MASS -matrixStats -wle -class -pdist ## ADDITIONAL SOFTWARE REQUIRED ## MIP SOLVERS: - COIN-OR Clp (>= 1.12.0) - IBM ILOG CPLEX (>= 12.1) ## Description for function l1pcahp This method implement the L1-PCAhp solution introduced in : "Robust Principal Component Analysis by Reverse Iterative Linear Programming" Andrea Visentin, Steven Prestwich and Armagan Tarim ## Usage l1pcahp(X, projDim=1, center=TRUE, projPoints=FALSE, scores=TRUE, initialize="l2pca", threshold=1.0) ## Arguments X data, must be in matrix or table formdata, must be in matrix or table form projDim number of dimensions to project data into, must be an integer, default is 1 center whether to center the data using the median, default is TRUE projPoints whether to calculate the projected points in terms of original coordinates (reconstruction),default is FALSE scores whether to return the matrix of scores, default is TRUE initialize method for initial guess for loadings matrix. Options are: "l2pca" - use traditional PCA/SVD, "random" - use a randomly-generated matrix threshold sets the convergence tolerance for the algorithm, default is 0.001
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