Parallel Fast Simulated Annealing Algorithm for Linear ...

Scope of our research
• We have developed an universal unsupervised data dimensionality reduction
technique, in some aspects similar to Principal Components Analysis (it’s
linear) and Multidimensional Scaling (it’s distance-preserving). What is more,
we try to reduce data sample length at the same time
• Establishing exact form of the transformation matrix is treated as a
continuous optimization problem and solved by Parallel Fast Simulated
Annealing.
• The algorithm is supposed to be used in conjunction with various procedures
of data mining e.g. outlier detection, cluster analysis, and classification.
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