spectral clustering - houston ml meetup
TRANSCRIPT
Clustering for New Discovery in DataPart 2
Houston Machine Learning Meetup
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https://energyconferencenetwork.com/machine-learning-oil-gas-2017/
20% off, PROMO code: HML
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Roadmap: Method
• Tour of machine learning algorithms (1 session)• Feature engineering (1 session)
– Feature selection - Yan
• Supervised learning (4 sessions)– Regression models -Yan– SVM and kernel SVM - Yan– Tree-based models - Dario– Bayesian method - Xiaoyang– Ensemble models - Yan
• Unsupervised learning (3 sessions)– K-means clustering – DBSCAN - Cheng– Mean shift – Agglomerative clustering – Kunal– Spectral clustering – Yan– Dimension reduction for data visualization - Yan
• Deep learning (4 sessions) _ Neural network
– From neural network to deep learning – Convolutional neural network– Train deep nets with open-source tools
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Roadmap: Application
• Business analytics
• Recommendation system
• Natural language processing
• Computer vision
• Energy industry
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Clustering Algorithm
• K-Means (King of clustering, many variants)
• DBSCAN (group neighboring points)
• Hierarchical clustering (a hierarchical structure, multiple levels)
• Mean shift (locating the maxima of density)
• Spectral clustering (cares about connectivity instead of proximity)
• Expectation Maximization (k-means is a variant of EM)
• Latent Dirichlet Allocation (natural language processing)
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Agenda
• Hierarchical clustering
• Mean shift
• Spectral clustering
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Referenece: http://www.cvl.isy.liu.se:82/education/graduate/spectral-clustering.html
Spectral Clustering
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Add noise to A
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Modification I
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Min-cut problem
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Modification II
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Ratio-cut problem
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Normalized spectral clustering
Make the clustering less sensitive to the cluster sizes
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Normalized symmetric spectral clustering
Less sensitive to the cluster sizes and better separation of clusters
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Summary
• Application of spectral clustering in computer vision
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Roadmap: Method
• Tour of machine learning algorithms (1 session)• Feature engineering (1 session)
– Feature selection - Yan
• Supervised learning (4 sessions)– Regression models -Yan– SVM and kernel SVM - Yan– Tree-based models - Dario– Bayesian method - Xiaoyang– Ensemble models - Yan
• Unsupervised learning (3 sessions)– K-means clustering – DBSCAN - Cheng– Mean shift – Agglomerative clustering – Spectral clustering – Kunal, Yan– Dimension reduction for data visualization - Yan
• Deep learning (4 sessions) _ Neural network
– From neural network to deep learning – Convolutional neural network– Train deep nets with open-source tools
28SCR©
https://energyconferencenetwork.com/machine-learning-oil-gas-2017/
20% off, PROMO code: HML
29SCR©
Thank you
Slides will be posted on slide share:
http://www.slideshare.net/xuyangela
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Parking We are here @ W205
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