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ETHEM ALPAYDIN The MIT Press, 2010
 

Summary: ETHEM ALPAYDIN
© The MIT Press, 2010
alpaydin@boun.edu.tr
http://www.cmpe.boun.edu.tr/~ethem/i2ml2e
Lecture Slides for
Why Reduce Dimensionality?
Reduces time complexity: Less computation
Reduces space complexity: Less parameters
Saves the cost of observing the feature
Simpler models are more robust on small datasets
More interpretable; simpler explanation
Data visualization (structure, groups, outliers, etc) if
plotted in 2 or 3 dimensions
3Lecture Notes for E Alpaydin 2010 Introduction to Machine Learning 2e © The MIT Press (V1.0)
Feature Selection vs Extraction
Feature selection: Choosing k ignoring the remaining d ­ k
Subset selection algorithms
Feature extraction: Project the
original xi , i =1,...,d dimensions to

  

Source: Alpaydın, Ethem - Department of Computer Engineering, Bogaziçi University

 

Collections: Computer Technologies and Information Sciences