Feature Extraction and Data Dimensionality Reduction Using MATLAB
Feature Extraction and Data Dimensionality Reduction Implementations Using MATLAB Algorithms
MATLAB Implementation of Discrete Cosine Transform for Pattern Recognition
This project demonstrates data dimensionality reduction using Discrete Cosine Transform combined with Principal Component Analysis, applicable to pattern recognition tasks like face recognition, palmprint analysis, expression classification, and fingerprint identification. The implementation involves signal transformation followed by feature extraction techniques.
PCA-KMeans Clustering Analysis with Dimensionality Reduction
PCA-KMeans clustering workflow: implementing dimensionality reduction on Wine dataset from UCI repository followed by clustering analysis for pattern discovery
Discrete Wavelet Transform Followed by Principal Component Analysis for Pattern Recognition Applications
This approach employs Discrete Wavelet Transform (DWT) for feature extraction and Principal Component Analysis (PCA) for dimensionality reduction, applicable to pattern recognition systems including facial recognition, palmprint analysis, emotion detection, and fingerprint identification.
MATLAB Implementation of Principal Component Analysis
Principal Component Analysis algorithm for image feature extraction and data dimensionality reduction applications
MATLAB Code Implementation for Principal Component Analysis (PCA)
An efficient PCA program for data dimensionality reduction, noise removal, and feature extraction. The implementation includes covariance matrix computation and eigenvalue decomposition for optimal component selection.
LPP for Dimensionality Reduction in Data Analysis
LPP is a highly effective tool for data dimensionality reduction, offering practical applications in various data processing scenarios. It is implemented through an algorithm that preserves local data structures while reducing computational complexity.
Orthogonal Linear Discriminant Analysis (OLDA)
Orthogonal Linear Discriminant Analysis (OLDA) can be applied to data dimensionality reduction, with key algorithmic differences from traditional LDA through orthogonal projection methods that can be implemented using functions like MATLAB's 'orth' or Python's numpy.linalg.svd for basis calculation.
Foreign-Implemented Spectral Clustering Algorithm
Foreign-developed implementation of spectral clustering algorithm with optimized computational approaches