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Pattern Recognition Final Task

Pattern Recognition Final Task. Ibrahim Arief – 185099 Timo Eckhard – 185126 University of Joensuu December 17 th , 2009. Contents. M-Fold-Cross Training Color Data Preprocessing Bayesian Classifier Multilayer Perceptron K-Means Clustering Speech Data Preprocessing

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Pattern Recognition Final Task

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  1. Pattern RecognitionFinal Task Ibrahim Arief – 185099 Timo Eckhard – 185126 University of Joensuu December 17th, 2009

  2. Contents • M-Fold-Cross Training • Color Data • Preprocessing • Bayesian Classifier • Multilayer Perceptron • K-Means Clustering • Speech Data • Preprocessing • Bayesian Classifier • Multilayer Perceptron • K-Means Clustering • Summary

  3. M-Fold-Cross Training • Partition into M subsets • One subset is assigned as test subset, the rest is training subset • We use the training subset for testing against test subset • Assign other subset as new test subset, the rest is training subset for that particular one • Repeat until all partition took their turn being tested

  4. Spectral Color Data - Preprocessing

  5. Spectral Color Data – Bayesian Classifier (1) • Raw spectral input – all classified to class 3

  6. Spectral Color Data – Bayesian Classifier (2) • Preprocessing : Tristimulus • Nice clumping, linearly separable

  7. Spectral Color Data – Bayesian Classifier (3) • Very high accuracy : 99.97%

  8. Spectral Color Data – Multi Layer Perceptron • Raw spectral data as input : ~5% • Tristimulus as input : ~30% • Question : parameters? • Answer : exhaustive search?

  9. Spectral Color Data – K-Means Clustering

  10. Speech Data – Preprocessing (1) • MFCC – Timeseries? • Plot of coefficients within a class

  11. Speech Data – Preprocessing (2) • Plot of variance for each coefficient

  12. Speech Data – Preprocessing (3) • Plot of bayesian accuracy for n-least-varied

  13. Speech Data – Preprocessing (4) • Delta-coefficients • Source: http://cslu.cse.ogi.edu/fsj/issues/issue5/sparse-ann/PhoneProbEst.html • Formula • Dimensionality reduction

  14. Speech Data – Bayesian Classifier • Frequency matters • No risk matrix • Raw accuracy : 18.13% • Delta-coefficient preprocessing : 96.06%

  15. Speech Data – Multi Layer Perceptron • Hidden Neuron : 22 • Normalized Raw Data : 20.25% • Reduced dimension, delta coefficient : 29.52% • Delta coefficient without reduced dimension : 27.84%

  16. Summary – Spectral Color Data • Bayesian Classifier • Raw Data : 3.92% • Preprocessed : 99.97% (tristimulus) • Multi Layer Perceptron • Raw Data : ~5% • Preprocessed : 58.1% (tristimulus) 99.7% (tristimulus + CIELAB + sRGB) • K-Means Clustering • Raw data : 92% • Preprocessed : 95%

  17. Summary – Speech Data • Bayesian Classifier • Raw Data : 18.19% • Preprocessed : 96.09% (delta-derivative, high variance elimination) • Multi Layer Perceptron • Raw Data : 20.25% • Preprocessed : 29.52% (delta-derivative, high variance elimination) • K-Means Clustering • Raw data : 24% • Preprocessed : 62% (normalized, delta-derivative)

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