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Spectral clustering - Houston ML Meetup
This document outlines the roadmap and agenda for a machine learning meetup covering clustering algorithms. The meetup will include sessions on k-means clustering, DBSCAN, hierarchical clustering, mean shift, spectral clustering and dimension reduction. Spectral clustering will be covered in two sessions focusing on the mathematical foundations and applications in computer vision. The meetup aims to provide an overview of machine learning techniques and their applications in domains such as business analytics, recommendation systems, natural language processing and the energy industry.
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Similar to Spectral clustering - Houston ML Meetup
Spectral clustering - Houston ML Meetup
- 1. Clustering for NewDiscovery in Data Part 2 Houston Machine Learning Meetup
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- 3. 3 SCR© Roadmap: Method • Tourof 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
- 4. 4 SCR© Roadmap: Application • Businessanalytics • Recommendation system • Natural language processing • Computer vision • Energy industry
- 5. 5 SCR© 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) ……
- 6. 6 SCR© Agenda • Hierarchical clustering •Mean shift • Spectral clustering
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- 21. 21 SCR© Normalized spectral clustering Makethe clustering less sensitive to the cluster sizes
- 22. 22 SCR© Normalized symmetric spectralclustering Less sensitive to the cluster sizes and better separation of clusters
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- 26. 26 SCR© Summary • Application ofspectral clustering in computer vision
- 27. 27 SCR© Roadmap: Method • Tourof 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
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- 29. 29 SCR© Thank you Slides willbe posted on slide share: http://www.slideshare.net/xuyangela
- 30. 30 SCR© ParkingWe are here @W205 Enter