Fast Best-Subset Selection Library
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Updated
Sep 14, 2024 - C++
Fast Best-Subset Selection Library
LiDAR processing ROS2. Segmentation: "Fast Ground Segmentation for 3D LiDAR Point Cloud Based on Jump-Convolution-Process". Clustering: "Curved-Voxel Clustering for Accurate Segmentation of 3D LiDAR Point Clouds with Real-Time Performance".
PCA and normal mode analysis of proteins
MODE-TASK plugin for PyMOL
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.
A graphical software for interactive rotational factor analysis and visualization of two-way data, mainly intended for vibrational spectra.
A simple machine learning library.
A C++ based face recognition implementation using Principal Component Analysis (PCA) to reduce dimensionality.
KNN, KMeans, Decision Tree, Naive Bayesian, Linear Regression, Principal Component Analysis, Neural Networks, Support Vector Machines all written in C++ from scratch.
Machine Learning algorithms in 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.
Python and C/C++ library for fast, accurate PCA on the GPU
A C++ implementation of the PageRank Algorithm using a hand-built CSR matrix data structure.
Graphical software that uses PCA to detect collisions of 3D models
Principal component analysis for 2D points
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