Data Fusion

Data fusion is about a systematic, joint analysis of data from different sources (measurement approaches) aiming to harvest their synergy. Both a major strength as well as a potential pitfall of data fusion consists of the fact that there is no magic bullet, no universal solution. – The careful analysis of the task at hand and suitable adaptation of the analysis strategy becomes as important as the analysis methodology itself.


General Theory



Pattern Recognition

Kernel Approaches

Sensor and Information Fusion


  • Matlab Statistics Toolbox contains e.g. canonical correlation analysis, Procrustes analysis, PCA and PLS
  • Multi-block Toolbox for Matlab by Frans van den Berg
  • CuBatch Comprehensive Matlab toolbox for batch modelling and multi-way analysis, see the article by S. Gourvénec et al.
  • Simca-P+ incorporates the patented O2-PLS algorithm
  • PRTools Matlab toolbox for pattern recognition including classifier combination
  • Shogun Machine learning toolbox including multiple kernel learning
  • CCA Canonical Correlation Analysis
  • ade4  Co-inertia analysis by S. Dray, A.B. Dufour and D. Chessel
  • MADE4 Co-inertia analysis for microarray analysis by A.C. Culhane et al.

Data Sets



Dr. Juergen von Frese
Data Analysis Solutions
DA-Sol GmbH

Telephone: +49 8143 9977293
Fax: +49 8143 9977294