CSC3022H: Machine Learning Lab 3: Principal Component Analysis (PCA)
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Updated
Dec 12, 2018 - C++
CSC3022H: Machine Learning Lab 3: Principal Component Analysis (PCA)
Contains the codes for Extended Histogram of Gradients for object recognition developed by me during my PhD studies.
A C++ based face recognition implementation using Principal Component Analysis (PCA) to reduce dimensionality.
A simple machine learning library.
Machine Learning algorithms in C++
MODE-TASK plugin for PyMOL
KNN, KMeans, Decision Tree, Naive Bayesian, Linear Regression, Principal Component Analysis, Neural Networks, Support Vector Machines all written in C++ from scratch.
A C++ implementation of the PageRank Algorithm using a hand-built CSR matrix data structure.
Principal component analysis for 2D points
A graphical software for interactive rotational factor analysis and visualization of two-way data, mainly intended for vibrational spectra.
Graphical software that uses PCA to detect collisions of 3D models
TeraPCA is a multithreaded C++ software suite based on Intel's MKL library (or any other BLAS and/or LAPACK distribution). TeraPCA features no dependencies to external libraries and combines the robustness of subspace iteration with the power of randomization.
PCA and normal mode analysis of proteins
Python and C/C++ library for fast, accurate PCA on the GPU
Fast Best-Subset Selection Library
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