LDA Beginner's Tutorial
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Introduction to Latent Dirichlet Allocation (LDA). We cover the basic ideas necessary to understand LDA then construct the model from its generative process. Intuitions are emphasized but little guidance is given for fitting the model which is not very insightful.
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LDA Beginner's Tutorial
- 1. ©2013 LinkedIn Corporation. All Rights Reserved. Latent Dirichlet Allocation (LDA) - for ML-IR Discussion Group 1 Prepared by Wayne Tai Lee, Satpreet Singh
- 2. ©2013 LinkedIn Corporation. All Rights Reserved. Latent Dirichlet Allocation: A Bayesian Unsupervised Learning Model Roadmap 2 • Unsupervised learning • Bayesian Statistics • Mixture Models • LDA – theory and intuition • LDA – practice and applications
- 3. ©2013 LinkedIn Corporation. All Rights Reserved. Unsupervised Learning Learning patterns with no labels 3 • Clustering is a form of “Unsupervised learning” • Classification is known as supervised learning • Validation is difficult
- 4. ©2013 LinkedIn Corporation. All Rights Reserved. 4 How would you cluster?
- 5. ©2013 LinkedIn Corporation. All Rights Reserved. 5 Documents of wikipedia Now try these ones!
- 6. ©2013 LinkedIn Corporation. All Rights Reserved. Bayesian Statistics A framework to update your beliefs 6 • Probabilities as beliefs • Updates your belief as data is observed • Requires a model that describes the data generation
- 7. ©2013 LinkedIn Corporation. All Rights Reserved. 7 Candidate potential Example: Evaluating Candidates
- 8. ©2013 LinkedIn Corporation. All Rights Reserved. 8 Candidate potential Example: Evaluating Candidates Schooling Experience Interview Internship
- 9. ©2013 LinkedIn Corporation. All Rights Reserved. 9 Candidate potential Example: Evaluating Candidates Schooling Experience Interview Internship How to update?!
- 10. ©2013 LinkedIn Corporation. All Rights Reserved. 10
- 11. ©2013 LinkedIn Corporation. All Rights Reserved. 11 Model for candidates Model for data generation
- 12. ©2013 LinkedIn Corporation. All Rights Reserved. Mixture Models A popular statistical model 12 • An easy way to build hierarchical relationships
- 13. ©2013 LinkedIn Corporation. All Rights Reserved. Mixture models visualized 13 Candidate Quality High Low
- 14. ©2013 LinkedIn Corporation. All Rights Reserved. 14 Marginal Distribution of Candidate Performance: ignore quality
- 15. ©2013 LinkedIn Corporation. All Rights Reserved. 15 Distribution of Candidate Performance:
- 16. ©2013 LinkedIn Corporation. All Rights Reserved. 16 Distribution of Candidate Performance: Mixture Weights
- 17. ©2013 LinkedIn Corporation. All Rights Reserved. 17 Mixture Weights Distribution of Candidate Performance:
- 18. ©2013 LinkedIn Corporation. All Rights Reserved. 18 Distribution of Candidate Performance: ? ? ? ?
- 19. ©2013 LinkedIn Corporation. All Rights Reserved. How are words in a document generated? 19
- 20. ©2013 LinkedIn Corporation. All Rights Reserved. One possibility: 20 Each word comes from different topics (bag of words: ignore order)
- 21. ©2013 LinkedIn Corporation. All Rights Reserved. How are words in a document generated? 21 Each word comes from different topics Mixture Weight for Topic k Multinomial Distribution over ALL words based on topic k
- 22. ©2013 LinkedIn Corporation. All Rights Reserved. Just a mixture model 22 Word Topic 1 Topic K Leadership Big Data Machine Learning
- 23. ©2013 LinkedIn Corporation. All Rights Reserved. Just a mixture model 23 Word Topic 1 Topic K Leadership Big Data Machine Learning 1) Pick a topic 2) Pick a word
- 24. ©2013 LinkedIn Corporation. All Rights Reserved. Just a mixture model 24 Word Topic 1 Topic K Leadership Big Data Machine Learning The chosen Topic: Z
- 25. ©2013 LinkedIn Corporation. All Rights Reserved. Just a mixture model 25 Word Topic 1 Topic K Leadership Big Data Machine Learning So we really want to know 1) Z 2) _ 3) _ The chosen Topic: Z
- 26. ©2013 LinkedIn Corporation. All Rights Reserved. Just a mixture model 26 Word Topic 1 Topic K Leadership Big Data Machine Learning So we really want to know 1) Z (cluster for the word) 2) (document composition) 3) (key words) The chosen Topic: Z
- 27. ©2013 LinkedIn Corporation. All Rights Reserved. Review! 27 Z W
- 28. ©2013 LinkedIn Corporation. All Rights Reserved. 28 Zd,n k=1…K Wd,n n=1,…,Nd d=1,…,D K: number of topics Nd: number of words D: number of documents
- 29. ©2013 LinkedIn Corporation. All Rights Reserved. 29 Zd,n k=1…K Wd,n n=1,…,Nd d=1,…,D K: number of topics Nd: number of words D: number of documents Bayesian: But what about the distribution for and ??
- 30. ©2013 LinkedIn Corporation. All Rights Reserved. 30 Zd,n k=1…K Wd,n n=1,…,Nd d=1,…,D K: number of topics Nd: number of words D: number of documents Bayesian: But what about the distribution for and ??
- 31. ©2013 LinkedIn Corporation. All Rights Reserved. 31 and control the “sparsity” of the weights for the multinomial. Implications: a priori we assume - Topics have few key words - Documents only have a small subset of topics
- 32. ©2013 LinkedIn Corporation. All Rights Reserved. Dirichlet Distribution with Different Sparsity Parameters 32
- 33. ©2013 LinkedIn Corporation. All Rights Reserved. 33 Latent Dirichlet Allocation!!! Zd,n k=1…K Wd,n n=1,…,Nd
- 34. ©2013 LinkedIn Corporation. All Rights Reserved. 34 How do we fit this model? Want the posterior: Worst part of Bayesian Analysis…..personally speaking~
- 35. ©2013 LinkedIn Corporation. All Rights Reserved. 35 Two main ways to get posterior: - Sampling methods - Asymtotically correct - Time consuming - Lots of black magic in sampling tricks - Variational methods (practical solution!) - An approximation with no guarantees - Faster - Need math skills
- 36. ©2013 LinkedIn Corporation. All Rights Reserved. 36 Variational Bayes (specifically mean field variational bayes): What’s crazy? - Assumes all the latent variables are independent What’s not crazy? - Finds the “best” model within this crazy class. - Best under KL divergence Empirically have shown promising results! For “sufficient” details: “Explaining Variational Approximations ” by Ormerod and Wand
- 37. ©2013 LinkedIn Corporation. All Rights Reserved. LDA Take Home 37 - An intuitively appealing Bayesian unsupervised learning model - Training is difficult - Lots of packages exist, main issue is scalability - Validation is difficult - Usually cast into a supervised learning framework - Presentation is difficult - Visualization for the Bayesian model is hard.
Notas del editor
- Take home: validation is difficult….no true answer here.
- Clustering documents is difficult because many repeated words are used. Some documents may be similar to one another on different topics. So we might want to cluster allowing membership.
- 2 stage process
- Example: the word usage of “professional” is probably higher in the topic of professional network than a social network.
- 2 stage process
- 2 stage process
- 2 stage process
- 2 stage process
- 2 stage process
- Example: the word usage of “professional” is probably higher in the topic of professional network than a social network.
- Example: the word usage of “professional” is probably higher in the topic of professional network than a social network.
- Example: the word usage of “professional” is probably higher in the topic of professional network than a social network.
- Example: the word usage of “professional” is probably higher in the topic of professional network than a social network.
- Example: the word usage of “professional” is probably higher in the topic of professional network than a social network.
- Example: the word usage of “professional” is probably higher in the topic of professional network than a social network.
- Example: the word usage of “professional” is probably higher in the topic of professional network than a social network.
- Example: the word usage of “professional” is probably higher in the topic of professional network than a social network.
- Example: the word usage of “professional” is probably higher in the topic of professional network than a social network.