Projects authored by franz kiraly.


Logo IPCA v0.1

by kiraly - July 7, 2014, 10:25:03 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 9606 views, 2707 downloads, 0 subscriptions

About: This package implements Ideal PCA in MATLAB. Ideal PCA is a (cross-)kernel based feature extraction algorithm which is (a) a faster alternative to kernel PCA and (b) a method to learn data manifold certifying features.

Changes:

Initial Announcement on mloss.org.


Logo AlCoCoMa 1.0

by kiraly - November 8, 2013, 09:38:07 CET [ BibTeX BibTeX for corresponding Paper Download ] 7781 views, 2041 downloads, 0 subscriptions

About: ALgebraic COmbinatorial COmpletion of MAtrices. A collection of algorithms to impute or denoise single entries in an incomplete rank one matrix, to determine for which entries this is possible with any algorithm, and to provide algorithm-independent error estimates. Includes demo scripts.

Changes:

Initial Announcement on mloss.org.


Logo AROFAC 1.0

by kiraly - August 5, 2013, 10:56:21 CET [ BibTeX BibTeX for corresponding Paper Download ] 8787 views, 2251 downloads, 0 subscriptions

About: Approximate Rank One FACtorization of tensors. An algorithm for factorization of three-way-tensors and determination of their rank, includes example applications.

Changes:

Initial Announcement on mloss.org.


Logo JMLR SSA Toolbox 1.3

by paulbuenau - January 24, 2012, 15:51:02 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 49336 views, 12766 downloads, 0 subscriptions

About: The SSA Toolbox is an efficient, platform-independent, standalone implementation of the Stationary Subspace Analysis algorithm with a friendly graphical user interface and a bridge to Matlab. Stationary Subspace Analysis (SSA) is a general purpose algorithm for the explorative analysis of non-stationary data, i.e. data whose statistical properties change over time. SSA helps to detect, investigate and visualize temporal changes in complex high-dimensional data sets.

Changes:
  • Various bugfixes.