Parallel Fast Simulated Annealing Algorithm for Linear ...

Conclusion
• The algorithm was tested for numerous instances of outlier detection,
cluster analysis and classification problems and was found to offer promising
performance. It results in an accurate distance preservation with possibility
of out-of-sample extension at the same time.
• Drawbacks
It is not designed for huge datasets (due to significant computational cost of
cost function evaluation) and shouldn’t be used in the situation where only
single data analysis task needs to be performed.
• What can be done in the future
We observed that taking into account topological deformation of the dataset in the reduced
feature space (by proposed weighting scheme) brings positive results in various data mining
procedures. It can be easily extended for other DR techniques!
Proposed approach could make algorithms very prone to ‘curse of dimensionality’ practically
usable (we have examined it in the case of KDE).
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