SimpleMatrix

About

SimpleMatrix is an extremely lightweight matrix library, containing a single header file.

It uses the namespace smat.
The Matrix class is a template class.
It implements basic matrix representations and operations, such as multiplication, transpose, and submatrix.
It does NOT implement complicated operations such as inverse, determinant, eigenvector, or decompositions.
It implements the Multidimensional Scaling (MDS) algorithm.

This library is developed as part of the MDS Feature Learning project.

The wiki site of the SimpleMatrix C++ library: https://sites.google.com/site/simpmatrix/
The wiki site of the MDS Feature Learning project: https://sites.google.com/site/mdsfeature/

To give credit to this library, you may cite:

@inproceedings{wang2013feature,
  title={Feature learning by multidimensional scaling and its applications in object recognition},
  author={Wang, Quan and Boyer, Kim L},
  booktitle={Graphics, Patterns and Images (SIBGRAPI), 2013 26th SIBGRAPI-Conference on},
  pages={8--15},
  year={2013},
  organization={IEEE}
}

Files

SimpleMatrix.h

This is an extremely lightweight matrix library, containing a single header file.
It uses the namespace smat.
The Matrix class is a template class.
It implements basic matrix representations and operations, such as multiplication, transpose, and submatrix.
It does NOT implement complicated operations such as inverse, determinant, eigenvector, or decompositions.
It implements the Multidimensional Scaling (MDS) algorithm with two versions: the UCF version and the SMACOF version.

demo_Matrix.cpp

This is a demo showing how to use this library, including reading matrix from txt file, matrix multiplication, and MDS.
A Makefile is provided to compile this demo with g++.

visualize_MDS_results.m

This is a MATLAB script for showing the MDS results of running demo_Matrix.exe.
The demo_Matrix.exe performs MDS on the matrix in swissD.txt, which is the geodesic distance matrix of a Swiss roll surface.
The swissX0.txt contains the initialization matrix for the Swiss roll flattening problem.
The MDS results are saved in swissX1.txt, swissX2.txt, swissX3.txt and swissX4.txt.
swissroll.mat stores data for the visualization.

Copyright

Copyright (C) 2013 Quan Wang <wangq10@rpi.edu>
Signal Analysis and Machine Perception Laboratory
Department of Electrical, Computer, and Systems Engineering
Rensselaer Polytechnic Institute, Troy, NY 12180, USA