MATLAB code for dimensionality reduction, feature extraction, fault detection, and fault diagnosis using Kernel Principal Component Analysis (KPCA).
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Updated
Feb 21, 2022 - MATLAB
MATLAB code for dimensionality reduction, feature extraction, fault detection, and fault diagnosis using Kernel Principal Component Analysis (KPCA).
Federated Principal Component Analysis Revisited!
Demonstration on how binary grey wolf optimization (BGWO) applied in the feature selection task.
Dynamic Mode Decomposition (DMD)
Benchmark of online PCA algorithms
Koopman operator
Streaming, Memory-Limited, r-truncated SVD Revisited!
Simple, fast and ease of implementation. The filter feature selection methods include Relief-F, PCC, TV, and NCA.
Predictive principal component analysis (PredPCA)
🦀🦀🦀 Sort spikes from extra-cellular recordings using neural networks. Fully automated.
DataHigh: A graphical user interface for visualizing and interacting with high-dimensional neural activity
Auto-UFSTool - An Automatic MATLAB Toolbox for Unsupervised Feature Selection
Optimal Projections for Clustering
Spectral embedding using Laplacian Eigenmaps
morgen - Model Order Reduction for Gas and Energy Networks
Exploiting Multi-Layer Features Using a CNN-RNN Approach for RGB-D Object Recognition (ECCV 2018 workshops)
The binary version of Harris Hawk Optimization (HHO), called Binary Harris Hawk Optimization (BHHO) is applied for feature selection tasks.
The binary version of Differential Evolution (DE), named as Binary Differential Evolution (BDE) is applied for feature selection tasks.
source code for MvBLS paper
Dimensionality reduction based on distance preservation to local mean for symmetric positive definite matrices
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