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Unsupervised Learning of Prototypes and Attribute Weights

Unsupervised Learning of Prototypes and Attribute Weights. Advisor : Dr. Hsu Presenter : Yu Cheng Chen Author: Hichem Frigui, Olfa Nasraoui. Transactions on Pattern Recognition 2004, Pages 567-581. Outline. Motivation Objective Introduction Background

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Unsupervised Learning of Prototypes and Attribute Weights

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  1. Unsupervised Learning of Prototypes and Attribute Weights Advisor :Dr. Hsu Presenter: Yu Cheng Chen Author: Hichem Frigui, Olfa Nasraoui Transactions on Pattern Recognition 2004, Pages 567-581

  2. Outline • Motivation • Objective • Introduction • Background • Simultaneous clustering and attribute discrimination • Application • Conclusions • Personal Opinion

  3. Motivation • The selected and weighted attributes can effect the learning algorithms significantly. • Several methods have been proposed for feature selection and weighting. • Assume feature relevance is invariant • Only appropriate for binary weighting • No methods exist for assigning different weights for distinct classes of a data set prior to clustering

  4. Objective • Propose a method to perform clustering and feature weighting simultaneously. • For different cluster, we assign different feature weights.

  5. Introduction • Illustrate the need for di1erent sets of feature weights for di1erent clusters. Dirt Dirt

  6. Background • Prototype-based clustering- Fuzzy C-mean • X = {xj| j = 1,…,N}be a set of N feature vectors. • B=(B1,…,Bc) represent the prototype set of C clusters. • uij is the menbership of point xj in cluster Bi. • Minimize the equation 2.

  7. Background • Prototype-based clustering- Fuzzy C-mean

  8. Background • Fuzzy C-mean • Cannot automatic determinate the optimum number of cluster • C has to be specified a priori. • CA (Competitive Agglomeration)

  9. Simultaneous clustering and attribute discrimination • Search for the optimal prototype parameters, B, and the optimal set of feature weights, V, simultaneously. • SCAD1 & SCAD2 • vik represents the relevance weight of feature k in cluster I • dijk = | xjk− cik|

  10. Simultaneous clustering and attribute discrimination • To optimize J1, with respect to V, we use the Lagrange multiplier technique.

  11. Simultaneous clustering and attribute discrimination • The choice of δi in Eq. (9) is important • If δi is too small, then the 1st term dominates • and only one feature in cluster i will be maximally relevant and assigned a weight of 1 • The remaining features get assigned 0 weights.

  12. Simultaneous clustering and attribute discrimination • Updated uij • Updated cik

  13. Simultaneous clustering and attribute discrimination • SCAD2 • q is referred as a “discrimination exponent”.

  14. Simultaneous clustering and attribute discrimination • SCAD2 • Updated uij

  15. Simultaneous clustering and attribute discrimination:unknown number of clusters • The objective functions in (5) and (21) complement each other, and can easily be combined into one objective function. • This algorithm is called SCAD2-CA.

  16. Application1:color image segmentation • We illustrate the ability of SCAD2 • real color images • Feature data extraction • Texture features: 3 attributes • Color features: 2 attributes • Position features: 2 attributes (x and y)

  17. Application1:color image segmentation Grass Dirt

  18. Application1:color image segmentation

  19. Application 2:supervised classification • We use SCAD2-CA for supervised classification. • Iris data set • the Wisconsin Breast Cancer data set • the Pima Indians Diabetes data set • the Heart Disease data set.

  20. Conclusions • We have proposed a new approach to perform clustering and feature weighting simultaneously. • SCAD2-CA can determine the “optimal” number of clusters automatically.

  21. Personal Opinion • Advantages • Take into account different feature weights in different cluster. • clustering and feature weighting simultaneously • Writing skill • Application • Should be applied the idea in our clustering algorithms • Limited • Only suit for numeric data. • Discussion • Clustering techniques are very hard to improve

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