pattern recognition final task
DESCRIPTION
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 - PowerPoint PPT PresentationTRANSCRIPT
Pattern RecognitionFinal Task
Ibrahim Arief – 185099Timo Eckhard – 185126
University of JoensuuDecember 17th, 2009
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
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
Spectral Color Data - Preprocessing
Spectral Color Data – Bayesian Classifier (1)
• Raw spectral input – all classified to class 31 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24
1 0 0 1 3 6 8 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 02 0 0 1 3 6 8 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 03 0 0 1 4 8 2 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 04 0 0 1 4 4 4 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 05 0 0 1 5 9 6 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 06 0 0 1 5 9 6 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 07 0 0 1 5 1 2 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 08 0 0 1 5 1 2 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 09 0 0 1 6 3 8 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 010 0 0 1 5 9 6 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 011 0 0 1 4 4 4 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 012 0 0 1 4 0 6 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 013 0 0 1 4 4 4 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 014 0 0 1 3 6 8 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 015 0 0 1 3 6 8 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 016 0 0 1 4 8 2 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 017 0 0 1 4 4 4 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 018 0 0 1 3 3 2 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 019 0 0 1 3 3 2 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 020 0 0 1 4 4 3 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 021 0 0 1 4 0 6 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 022 0 0 1 4 4 4 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 023 0 0 1 4 4 4 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 024 0 0 1 3 6 8 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Spectral Color Data – Bayesian Classifier (2)
• Preprocessing : Tristimulus• Nice clumping, linearly separable
Spectral Color Data – Bayesian Classifier (3)
• Very high accuracy : 99.97%
Spectral Color Data – Multi Layer Perceptron
• Raw spectral data as input : ~5%• Tristimulus as input : ~30%• Question : parameters?• Answer : exhaustive search?
softmax quasinew 16 33,96% softmax hmc 14 24,21%
softmax conjgrad 13 31,89% logistic hmc 14 23,87%
softmax scg 14 28,77% softmax conjgrad 11 23,37%
softmax scg 16 28,03% softmax scg 13 23,29%
softmax conjgrad 14 27,03% softmax conjgrad 10 22,64%
softmax conjgrad 16 26,27% softmax scg 11 21,49%
softmax hmc 16 26,05% logistic quasinew 13 21,33%
softmax hmc 15 25,81% logistic scg 15 21,20%
softmax quasinew 15 24,39% logistic quasinew 15 21,02%
softmax conjgrad 15 24,29% softmax quasinew 10 20,88%
Spectral Color Data – K-Means Clustering
Speech Data – Preprocessing (1)
• MFCC – Timeseries?• Plot of coefficients within a class
Speech Data – Preprocessing (2)
• Plot of variance for each coefficient
Speech Data – Preprocessing (3)
• Plot of bayesian accuracy for n-least-varied
Speech Data – Preprocessing (4)
• Delta-coefficients• Source:
http://cslu.cse.ogi.edu/fsj/issues/issue5/sparse-ann/PhoneProbEst.html
• Formula
• Dimensionality reduction1 2 3 4 5 6 7 8 9 10
1 2 3 4 5 6
Speech Data – Bayesian Classifier
• Frequency matters• No risk matrix• Raw accuracy : 18.13%• Delta-coefficient preprocessing : 96.06%
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%
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%
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)