Intelligent Database Systems Lab Presenter: Wu, Jhen-Wei Authors: Fabian Bürger, Josef Pauli ICPRAM. Representation Optimization with Feature Selection and Manifold Learning in a Holistic Classification Framework

Intelligent Database Systems Lab Outlines Motivation Objectives Methods Experiments Conclusions Comments 2

Intelligent Database Systems Lab Motivation The performance of a manifold learner heavily depends on the dataset. A recent research regarding the dimension reduction is turned out to be relatively slow and ineffective for high-dimensional datasets. 3

Intelligent Database Systems Lab Objectives Develop a framework that incorporates multiple manifold learning algorithms in a holistic classification pipeline. 4

Intelligent Database Systems Lab 5 Methods

Intelligent Database Systems Lab 6 Methods

Intelligent Database Systems Lab 7 Methods

Intelligent Database Systems Lab 8 Methods

Intelligent Database Systems Lab 9 Methods

Intelligent Database Systems Lab 10 Methods

Intelligent Database Systems Lab 11 Methods

Intelligent Database Systems Lab 12 Methods ES: (μ/ρ+λ)

Intelligent Database Systems Lab 13 Experiments

Intelligent Database Systems Lab 14 Experiments

Intelligent Database Systems Lab 15 Experiments

Intelligent Database Systems Lab 16 Experiments

Intelligent Database Systems Lab Conclusions The concept of multi-pipeline classifiers offers a better performance, but comes along with higher computational costs. 17

Intelligent Database Systems Lab Comments Contribution –Present a novel automatic optimization framework that incorporates multiple manifold learning algorithms in a holistic classification pipeline. Applications –Evolutionary Optimization, Classification 18