Ouyang Ruofei Topic Model Latent Dirichlet Allocation Ouyang Ruofei May. 10 2013 LDA.

Ouyang Ruofei

Topic Model Latent Dirichlet Allocation

Ouyang Ruofei

May. 10 2013

LDA

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Introduction

Ouyang Ruofei LDA

Parameters:

Inference:

data = latent pattern + noise

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Introduction

Ouyang Ruofei LDA

Parametric Model:

Nonparametric Model:

Number of parameters is fixed w.r.t. sample size

Number of parameters grows with sample size

Infinite dimensional parameter space

Problem Parameter

Density Estimation Distributions

Regression Functions

Clustering Partitions

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Clustering

Ouyang Ruofei LDA

1.Ironman 2.Thor 3.Hulk

Indicator variable for each data point

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Dirichlet process

Ouyang Ruofei LDA

Ironman: 3 times Thor: 2 times Hulk: 2 times

Without the likelihood, we know that:

1. There are three clusters

2. The distribution over three clusters

New data

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Dirichlet process

Ouyang Ruofei LDA

Dirichlet distribution:

pdf:

mean:

Example:

Dir(Ironman,Thor,Hulk)

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Dirichlet process

Ouyang Ruofei LDA

Dirichlet distribution: Multinomial distribution:

Conjugate prior

Posterior: Example:

Ironman Thor Hulk

Prior 3 2 2

Likelihood 100 300 200

Posterior 103 302 202

Pseudo count

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Dirichlet process

Ouyang Ruofei LDA

In our Avengers model, K=3 (Ironman, Thor, Hulk)

Dirichlet process:

However, this guy comes…

Dirichlet distribution can’t model this stupid guy

K = infinity

Nonparametrics here mean infinite number of clusters

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Dirichlet process

Ouyang Ruofei LDA

α: Pseudo counts in each cluster

G0: Base distribution of each cluster

A distribution over distributions

Dirichlet process:

AGiven any partition

Distribution template

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Dirichlet process

Ouyang Ruofei LDA

Construct Dirichlet process by CRP

In a restaurant, there are infinite number of tables.

Chinese restaurant process:

Costumer 1 seats at an unoccupied table with p=1.

Costumer N seats at table k with p=

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Dirichlet process

Ouyang Ruofei LDA

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Dirichlet process

Ouyang Ruofei LDA

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Dirichlet process

Ouyang Ruofei LDA

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Dirichlet process

Ouyang Ruofei LDA

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Dirichlet process

Ouyang Ruofei LDA

Customers : data

Tables : clusters

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Dirichlet process

Ouyang Ruofei LDA

Train the model by Gibbs sampling

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Dirichlet process

Ouyang Ruofei LDA

Train the model by Gibbs sampling

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Gibbs sampling

Ouyang Ruofei LDA

Gibbs sampling is a MCMC method to obtain a sequence of observations from a multivariate distribution

The intuition is to turn a multivariate problem into a sequence of univariate problem.

Multivariate:

Univariate:

In Dirichlet process,

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Gibbs sampling

Ouyang Ruofei LDA

Gibbs sampling pseudo code:

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Topic model

Ouyang Ruofei LDA

Document

Mixture of topics

we can read words

Latent variable

But,

topics words

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Topic model

Ouyang Ruofei LDA

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Topic model

Ouyang Ruofei LDA

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Topic model

Ouyang Ruofei LDA

word/topic count topic/doc count

topic of xij

observed wordother topics

other words

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Topic model

Ouyang Ruofei LDA

Apply Dirichlet process in topic model

Topic 1 Topic 2 Topic 3

Document P1 P2 P3

Topic 1 Topic 2 Topic 3

Word Q1 Q2 Q3

Learn the distribution of topics in a document

Learn the distribution of topics for a word

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Topic model

Ouyang Ruofei LDA

t1 t2 t3

d1

t1 t2 t3

d2

t1 t2 t3

d3

w1 w2 w3 w4

t1

t2

t3

topic/doc table word/topic table

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Topic model

Ouyang Ruofei LDA

Latent Dirichlet allocation:

Dirichlet mixture model:

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LDA Example

Ouyang Ruofei LDA

w: ipad apple itunes mirror queen joker ladygaga

t1: product

t2: storyt3: poker

d1: ipad apple itunes

d2: apple mirror queen

d3: queen joker ladygaga

d4: queen ladygaga mirror

In fact, the topics are latent

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LDA example

Ouyang Ruofei LDA



d3: queen joker ladygaga


ipad apple itunes mirror queen joker ladygaga

t1 1 1 2

t2 2 1 2

t3 1 1 1

sum 1 2 1 2 3 1 2

t1 t2 t3

d1 1 1 1

d2 1 2 0

d3 1 0 2

d4 1 2 0

1 2 3

2 1 2

3 3 1

2 1 2

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LDA example

Ouyang Ruofei LDA



d3: joker ladygaga



t1 1 1 2

t2 2 1 2

t3 1 1 1

sum 1 2 1 2 3 1 2

t1 t2 t3

d1 1 1 1

d2 1 2 0

d3 1 0 2

d4 1 2 0

1 2 3

2 1 2

3 1

2 1 2

queen

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LDA example

Ouyang Ruofei LDA



d3: joker ladygaga



t1 1 1 2

t2 2 1 2

t3 1 1-1 1

sum 1 2 1 2 3-1 1 2

t1 t2 t3

d1 1 1 1

d2 1 2 0

d3 1 0 2-1

d4 1 2 0

1 2 3

2 1 2

3 1

2 1 2

queen

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LDA example

Ouyang Ruofei LDA



d3: joker ladygaga



t1 1 1 2

t2 2 1 2

t3 1 0 1

sum 1 2 1 2 2 1 2

t1 t2 t3

d1 1 1 1

d2 1 2 0

d3 1 0 1

d4 1 2 0

1 2 3

2 1 2

3 1

2 1 2

queen

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LDA example

Ouyang Ruofei LDA



d3: joker ladygaga



t1 1 1 2

t2 2 1 2+1

t3 1 0 1

sum 1 2 1 2 2+1 1 2

t1 t2 t3

d1 1 1 1

d2 1 2 0

d3 1 0+1 1

d4 1 2 0

1 2 3

2 1 2

3 1

2 1 2

queen2

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Further

Ouyang Ruofei LDA

Dirichlet distribution prior: K topics

Alpha mainly controls the probability of a topic with few training data in the document.

Dirichlet process prior: infinite topics

Beta mainly controls the probability of a topic with few training data in the words.

Supervised

Unsupervised

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Further

Ouyang Ruofei LDA

Unrealistic bag of words assumption

Lose power law behavior

TNG, biLDA

Pitman Yor language model

David Blei has done an extensive survey on topic modelhttp://home.etf.rs/~bfurlan/publications/SURVEY-1.pdf

http://home.etf.rs/~bfurlan/publications/SURVEY-1.pdf

Q&A

Ouyang Ruofei LDA

Ouyang Ruofei Topic Model Latent Dirichlet Allocation Ouyang Ruofei May. 10 2013 LDA.

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