Laboratory of Computer and Information Science / Neural Networks Research Centre CIS Lab Helsinki University of Technology
Research page of Timo Similä

Research page of Timo Similä

I am currently working in privat sector but this page should have the up-to-date information on my scientific career.


I have worked in various research positions at the Laboratory of Computer and Information Science at Helsinki University of Technology from 2001 to 2007. My research interests included information visualization, dimensionality reduction, prediction and multivariate data analysis in general.


I was the course assistant for Digital Signal Processing and Filtering during the autumn term 2002 and the spring terms 2006 and 2007.


  1. Zhu Z, Similä T, Corona F. Supervised distance preserving projections. Neural Processing Letters. Volume 38, Number 3, Pages 445-463, 2013.
  2. Similä T, Tikka J. Combined input variable selection and model complexity control for nonlinear regression. Pattern Recognition Letters. Volume 30, Number 3, Pages 231-236, 2009.
  3. Similä T, Tikka J. Input selection and shrinkage in multiresponse linear regression. Computational Statistics & Data Analysis. Volume 52, Number 1, Pages 406-422, 2007.
  4. Similä T. Majorize-minimize algorithm for multiresponse sparse regression. IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP). Honolulu, HI, USA. April 15-20, 2007. Volume II, Pages 553-556.
  5. Hakala R, Similä T, Sirola M, Parviainen J. Process state and progress visualization using self-organizing map. International Conference on Intelligent Data Engineering and Automated Learning (IDEAL). Burgos, Spain. September 20-23, 2006. Pages 73-80.
  6. Similä T, Tikka J. Common subset selection of inputs in multiresponse regression. IEEE International Joint Conference on Neural Networks (IJCNN). Vancouver, BC, Canada. July 16-21, 2006. Pages 1908-1915.
  7. Similä T. Self-organizing map visualizing conditional quantile functions with multidimensional covariates. Computational Statistics & Data Analysis. Volume 50, Number 8, Pages 2097-2110, 2006.
  8. Similä T, Tikka J. Multiresponse sparse regression with application to multidimensional scaling. International Conference on Artificial Neural Networks (ICANN). Warsaw, Poland. September 11-15, 2005. Pages 97-102. (Matlab function mrsr.m)
  9. Similä T, Laine S. Visual approach to supervised variable selection by self-organizing map. International Journal of Neural Systems. Volume 15, Numbers 1-2, Pages 101-110, 2005.
  10. Similä T. Self-organizing map learning nonlinearly embedded manifolds. Information Visualization. Volume 4, Number 1, Pages 22-31, 2005.
  11. Laine S, Similä T. Using SOM-based data binning to support supervised variable selection. International Conference on Neural Information Processing (ICONIP). Science City, Calcutta, India. November 22-25, 2004. Pages 172-180.
  12. Hätönen K, Laine S, Similä T. Using the logsig-function to integrate expert knowledge to self-organizing map based analysis. IEEE International Workshop on Soft Computing in Industrial Applications (SMCia). Binghamton, NY, USA. June 23-25, 2003. Pages 145-150.


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