A Lightweight Implementation of Eigenfaces for Facial Recognition
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Updated
May 19, 2018 - Python
A Lightweight Implementation of Eigenfaces for Facial Recognition
Python code that solves the eigensystem associated with Hermitan matrices. Demonstrated by finding the the first few eigenvalues and the corresponding eigenvectors of the aharmonic oscillator Hamiltonian.
TSNE is state of art method for visualization of data and PCA is also used for visualization of higher dimensional data by finding the most dominant eigen vectors
This program is implemented as a project for EE 242 course, and it implements Normalized Power Iteration with Deflation algorithmm to calculate most dominant eigenvalue, its eigenvector and the second most dominant eigenvalue.
Visual Guide for Apparently Simple Linear Algebra Concepts using NUMPY
Class homework for Linear Algebra in Python
Linear Algebra using Python
Build early warning indicators with SAS
My first research paper
Source code for solving complex eigenvalue & eigenvector problems.
The Hari-Zimmermann complex generalized hyperbolic SVD and EVD.
(DEPRECATED) HOWTO: use LAPACK on a Swift app to compute the eigenvalues & eigenvectors on macOS/iOS/Linux
Repository for the HU-Berlin course Numerical Introductory Seminar
Visualizing high dimensional data with PCA and LDA.
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