Furong Huang, Ph.D. Candidate, UC Irvine at MLconf NYC - 4/15/16

Discovery of Latent Factors in High-dimensionalData Using Tensor Methods

Furong Huang

University of California, Irvine

Machine Learning Conference 2016 New York City

1 / 25

Machine Learning - Modern Challenges

Big Data Challenging Tasks

Success of Supervised Learning

Image classification Speech recognition Text processing

Computation power growth

Enormous labeled data

2 / 25

Machine Learning - Modern Challenges

Big Data Challenging Tasks

Real AI requires Unsupervised Learning

Filter bank learning Feature extraction Embeddings; Topics

2 / 25

Machine Learning - Modern Challenges

Big Data Challenging Tasks

Real AI requires Unsupervised Learning

Filter bank learning Feature extraction Embeddings; Topics

Summarize key features in data: Machines vs Humans

Foundation for successful supervised learning

2 / 25

Unsupervised Learning with Big Data

Information Extraction

High dimension observation vs Low dimension representation

Cell Types

Topics

Communities

3 / 25

Unsupervised Learning with Big Data

Information Extraction

High dimension observation vs Low dimension representation

Cell Types

Topics

Communities

Finding Needle In the Haystack Is Challenging3 / 25

Unsupervised Learning with Big Data

Information Extraction

Solution for Unsupervised Learning

A Unified Tensor Decomposition Framework

3 / 25

Automated Categorization of DocumentsMixed topics

Topics

Education

Crime

Sports

4 / 25

Community Extraction From Connectivity Graph

Mixed memberships

5 / 25

Tensor Methods Compared with Variational Inference

PubMed on Spark: 8 million docs

103

104

105

Perplexity

TensorVariational

0

2

4

6

8

10 ×104

RunningTim

e(s)

6 / 25

Tensor Methods Compared with Variational Inference

PubMed on Spark: 8 million docs

103

104

105

Perplexity

TensorVariational

0

2

4

6

8

10 ×104

RunningTim

e(s)

Facebook: n ∼ 20k Yelp: n ∼ 40k DBLP: n ∼ 1 million

10-2

10-1

100

101

Error/group

FB YP DBLPsub DBLP 102

103

104

105

106

RunningTim

es(s)

FB YP DBLPsub DBLP

6 / 25

Tensor Methods Compared with Variational Inference

PubMed on Spark: 8 million docs

103

104

105

Perplexity

TensorVariational

0

2

4

6

8

10 ×104

RunningTim

e(s)

Facebook: n ∼ 20k Yelp: n ∼ 40k DBLP: n ∼ 1 million

10-2

10-1

100

101

Error/group

FB YP DBLPsub DBLP 102

103

104

105

106

RunningTim

es(s)

FB YP DBLPsub DBLP

Orders

ofMag

nitude Fa

ster &

MoreAc

curat

e

“Online Tensor Methods for Learning Latent Variable Models”, F. Huang, U. Niranjan, M. Hakeem, A. Anandkumar, JMLR14.“Tensor Methods on Apache Spark”, by F. Huang, A. Anandkumar, Oct. 2015.

6 / 25

Cataloging Neuronal Cell Types In the Brain

7 / 25

Cataloging Neuronal Cell Types In the BrainOur method vs Average expression level [Grange 14’]

0.5

1.0

1.5

2.0

2.5

kSpatial point process (ours)

Average expression level ( )previous

Recovered known cell types

1 astrocytes

2 interneurons

3 oligodendrocytes

” Discovering Neuronal Cell Types and Their Gene Expression Profiles Using a Spatial Point Process Mixture Model ” by F.Huang, A. Anandkumar, C. Borgs, J. Chayes, E. Fraenkel, M. Hawrylycz, E. Lein, A. Ingrosso, S. Turaga, NIPS 2015 BigNeuro

workshop.

8 / 25

Word Sequence Embedding Extraction

football

soccer

tree

Word Embedding

The weather is good.

Her life spanned years of

incredible change for women.Mary lived through an era of

liberating reform for women.

Word Sequence Embedding

9 / 25

Word Sequence Embedding Extraction

football

soccer

tree

Word Embedding

The weather is good.

Her life spanned years of

incredible change for women.Mary lived through an era of

liberating reform for women.

Word Sequence Embedding

Paraphrase Detection

MSR paraphrase data: 5800 pairs of sentences

Method Outside Information F score

Vector Similarity (Baseline) word similarity 75.3%Convolutional Tensor (Proposed) none 80.7%Skip-thought (NIPS’15) train on large corpus 81.9%

“Convolutional Dictionary Learning through Tensor Factorization”, by F. Huang, A. Anandkumar, conference and workshopproceeding of JMLR, vol.44, Dec 2015.

9 / 25

Human Disease Hierarchy DiscoveryCMS: 1.6 million patients, 168 million diagnostic events, 11 k diseases.

” Scalable Latent TreeModel and its Application to Health Analytics ” by F. Huang, N. U.Niranjan, I. Perros, R. Chen, J. Sun,A. Anandkumar, NIPS 2015 MLHC workshop.

10 / 25

Unsupervised Learning via Probabilistic Models

Words

Topics

Choice Variable

life gene data DNA RNA

k1 k2 k3 k4 k5

h

A A A A A

Unlabeled data Probabilistic admixture model Learning Algorithm Inference

11 / 25

Unsupervised Learning via Probabilistic Models

Words

Topics

Choice Variable

life gene data DNA RNA

k1 k2 k3 k4 k5

h

A A A A A

Unlabeled data Probabilistic admixture model MCMC Inference

MCMC: random sampling, slow◮ Exponential mixing time

11 / 25

Unsupervised Learning via Probabilistic Models

Words

Topics

Choice Variable

life gene data DNA RNA

k1 k2 k3 k4 k5

h

A A A A A

Unlabeled data Probabilistic admixture model Likelihood Methods Inference

MCMC: random sampling, slow◮ Exponential mixing time

Likelihood: non-convex, not scalable◮ Exponential critical points

11 / 25

Unsupervised Learning via Probabilistic Models

Words

Topics

Choice Variable

life gene data DNA RNA

k1 k2 k3 k4 k5

h

A A A A A

Unlabeled data Probabilistic admixture model Likelihood Methods Inference

MCMC: random sampling, slow◮ Exponential mixing time

Likelihood: non-convex, not scalable◮ Exponential critical points

Solution

A unified tensor decomposition framework

11 / 25

Unsupervised Learning via Probabilistic Models

Words

Topics

Choice Variable

life gene data DNA RNA

k1 k2 k3 k4 k5

h

A A A A A

Unlabeled data Probabilistic admixture model Te���� �e���p������� Inference

= + +

tensor decomposition → correct model

12 / 25

Unsupervised Learning via Probabilistic Models

Words

Topics

Choice Variable

life gene data DNA RNA

k1 k2 k3 k4 k5

h

A A A A A

Unlabeled data Probabilistic admixture model T�� ����������� Inference

� � �

tensor decomposition → correct model

Contributions

Guaranteed online algorithm with global convergence guarantee

Highly scalable, highly parallel, random projection

Tensor library on CPU/GPU/Spark

Interdisciplinary applications

Extension to model with group invariance

12 / 25

What is a tensor?

Matrix: Second Order Moments

M2: pair-wise relationship.

[x⊗ x]i1,i2 = xi1xi2 → [M2]i1,i2

=

i1

i2

Tensor: Third Order Moments

M3: triple-wise relationship.

[x⊗ x⊗ x]i1,i2,i3 = xi1xi2xi3 → [M3]i1,i2,i3=

i1

i2

i3

13 / 25

Why are tensors powerful?

Matrix Orthogonal Decomposition

Not unique without eigenvalue gap[

1 00 1

]

= e1e⊤1+e2e

⊤2= u1u

⊤1+u2u

⊤2 e1

e2

u1 = [√2

2, −

√2

2]

u2 = [√2

2,√2

2]

14 / 25

Why are tensors powerful?

Matrix Orthogonal Decomposition

Not unique without eigenvalue gap[

1 00 1

]

= e1e⊤1+e2e

⊤2= u1u

⊤1+u2u

⊤2 e1

e2

u1 = [√2

2, −

√2

2]

u2 = [√2

2,√2

2]

Tensor Orthogonal Decomposition

Unique: eigenvalue gap not needed

+=

≠14 / 25

Why are tensors powerful?

Matrix Orthogonal Decomposition

Not unique without eigenvalue gap[

1 00 1

]

= e1e⊤1+e2e

⊤2= u1u

⊤1+u2u

⊤2 e1

e2

u1 = [√2

2, −

√2

2]

u2 = [√2

2,√2

2]

Tensor Orthogonal Decomposition

Unique: eigenvalue gap not needed

Slice of tensor has eigenvalue gap

+=

≠14 / 25

Why are tensors powerful?

Matrix Orthogonal Decomposition

Not unique without eigenvalue gap[

1 00 1

]

= e1e⊤1+e2e

⊤2= u1u

⊤1+u2u

⊤2 e1

e2

u1 = [√2

2, −

√2

2]

u2 = [√2

2,√2

2]

Tensor Orthogonal Decomposition

Unique: eigenvalue gap not needed

Slice of tensor has eigenvalue gap

+=

≠14 / 25

Outline

1 Introduction

2 LDA and Community ModelsFrom Data Aggregates to Model ParametersGuaranteed Online Algorithm

3 Conclusion

15 / 25

Outline

1 Introduction

2 LDA and Community ModelsFrom Data Aggregates to Model ParametersGuaranteed Online Algorithm

3 Conclusion

16 / 25

Probabilistic Topic Models - LDA

Bag of words

Topics

Topic Proportion

police

witness

campus

police

witness

campus

police

witness

campus

17 / 25

Probabilistic Topic Models - LDA

Bag of words

Topics

Topic Proportion

police

witness

campus

police

witness

campus

police

witness

campus

police

witness

crime

Sports

Educa�

on

campus

17 / 25

Probabilistic Topic Models - LDA

Bag of words

Topics

Topic Proportion

police

witness

campus

police

witness

campus

police

witness

campus

police

witness

crime

Sports

Educa�on

campus

Goalcampus

police

witness Topic-word matrix P[word = i|topic = j]

17 / 25

Mixture Form of Moments

Goal: Linearly independent topic-word tablecampus

police

witness

18 / 25

Mixture Form of Moments

Goal: Linearly independent topic-word tablecampus

police

witness

M1: Occurrence of Words

= + +campus

police

witness

crime

Sports

Educa�on

campus

police

witness

campus

police

witness

18 / 25

Mixture Form of Moments

Goal: Linearly independent topic-word tablecampus

police

witness

M1: Occurrence of Words

= + +campus

police

witness

crime

Sports

Educa�on

campus

police

witness

campus

police

witness

No unique decomposition of vectors

18 / 25

Mixture Form of Moments

Goal: Linearly independent topic-word tablecampus

police

witness

M2: Modified Co-occurrence of Word Pairs

= + +campus

police

witness

crime

Sports

Educa�on

campus

police

witness

campus

police

witness

18 / 25

Mixture Form of Moments

Goal: Linearly independent topic-word tablecampus

police

witness

M2: Modified Co-occurrence of Word Pairs

= + +campus

police

witness

crime

Sports

Educa�on

campus

police

witness

campus

police

witness

Matrix decomposition recovers subspace, not actual model

18 / 25

Mixture Form of Moments

Goal: Linearly independent topic-word table

Find a W W W W such that

M2: Modified Co-occurrence of Word Pairs

= + +campus

police

witness

crime

Sports

Educa�on

campus

police

witness

campus

police

witness

Many such W ’s, find one, project data with W

18 / 25

Mixture Form of Moments

Goal: Linearly independent topic-word table

Know a W W W W such that

M3: Modified Co-occurrence of Word Triplets

= + +campus

police

witness

crime

Sports

Educa�on

campus

police

witness

campus

police

witness

W

W

W

Unique orthogonal tensor decomposition, project result with W †

18 / 25

Mixture Form of Moments

Goal: Linearly independent topic-word table

Know a W W W W such that

M3: Modified Co-occurrence of Word Triplets

= + +campus

police

witness

crime

Sports

Educa�on

campus

police

witness

campus

police

witness

W

W

W

Tensor decomposition uniquely discovers the correct model

Learning Topic Models through Matrix/Tensor Decomposition

18 / 25

Mixed Membership Community Models

Mixed memberships

19 / 25

Mixed Membership Community Models

Mixed memberships

What ensures guaranteed learning?

� � �Alice

Bob

Charlie

Math

ema�

cians

Vegetarian

s

Musician

s

David

Ellen

Frank

Grace

Jack

Kathy

19 / 25

Mixed Membership Community Models

Mixed memberships

What ensures guaranteed learning?

� � �Alice

Bob

Charlie

Math

ema�cian

s

Vegetarian

s

Musician

s

David

Ellen

Frank

Grace

Jack

Kathy

19 / 25

Outline

1 Introduction

2 LDA and Community ModelsFrom Data Aggregates to Model ParametersGuaranteed Online Algorithm

3 Conclusion

20 / 25

How to do tensor decomposition?Model is uniquely identifiable! How to identify?

21 / 25

How to do tensor decomposition?How to find components? Non-convex optimization problem!

21 / 25

How to do tensor decomposition?How to find components? Non-convex optimization problem!

Objective Function

Theorem: We propose an objective function with equivalent local optima.

21 / 25

How to do tensor decomposition?How to find components? Non-convex optimization problem!

Objective Function

Theorem: We propose an objective function with equivalent local optima.

Saddle point: enemy of SGDSaddle Point

Saddle point has 0 gradient

21 / 25

How to do tensor decomposition?How to find components? Non-convex optimization problem!

Objective Function

Theorem: We propose an objective function with equivalent local optima.

Saddle point: enemy of SGDSaddle Point

Saddle point has 0 gradient

Non-degenerate saddle: Hessianhas ± eigenvalue

21 / 25

How to do tensor decomposition?How to find components? Non-convex optimization problem!

Objective Function

Theorem: We propose an objective function with equivalent local optima.

Saddle point: enemy of SGD

escape

stuckSaddle point has 0 gradient

Non-degenerate saddle: Hessianhas ± eigenvalue

Negative eigenvalue: direction ofescape

21 / 25

How to do tensor decomposition?How to find components? Non-convex optimization problem!

Objective Function

Theorem: We propose an objective function with equivalent local optima.

Saddle point: enemy of SGD

escape

stuckSaddle point has 0 gradient

Non-degenerate saddle: Hessianhas ± eigenvalue

Negative eigenvalue: direction ofescape

Guaranteed Global Converge Online Tensor Decomposition

Theorem: For smooth fn. with non-degenerate saddle points,noisy SGD converges to a local minimum in polynomial steps.

21 / 25

How to do tensor decomposition?How to find components? Non-convex optimization problem!

Objective Function

Theorem: We propose an objective function with equivalent local optima.

Saddle point: enemy of SGD

escape

stuckSaddle point has 0 gradient

Non-degenerate saddle: Hessianhas ± eigenvalue

Negative eigenvalue: direction ofescape

Guaranteed Global Converge Online Tensor Decomposition

Theorem: For smooth fn. with non-degenerate saddle points,noisy SGD converges to a local minimum in polynomial steps.

Noise could help!“Escaping From Saddle Points — Online Stochastic Gradient for Tensor Decomposition”,by R. Ge, F. Huang, C. Jin, Y. Yuan,COLT 2015.

21 / 25

Outline

1 Introduction

2 LDA and Community ModelsFrom Data Aggregates to Model ParametersGuaranteed Online Algorithm

3 Conclusion

22 / 25

Contributions

Spectral methods reveal hidden structure

Text/Image processing

Social networks

Neuroscience, heathcare ...

23 / 25

Contributions

Spectral methods reveal hidden structure

Text/Image processing

Social networks

Neuroscience, heathcare ...

Versatile for latent variable models

Flat model → hierarchical model

Sparse coding → convolutionalmodel

Efficient, convergence guarantee

� � �

� � � � � � � � �

� � � � � �M3

escape

stuck

23 / 25

Thank YouCollaborators

Anima Anandkumar

UC Irvine

Rong Ge

Duke University

Srini Turaga

Janelia Research

Chi Jin

UC Berkeley

Jennifer Chayes

MSR

Christian Borgs

MSR

Ernest Fraenkel

MIT

Yang Yuan

Cornell U

UN Niranjan

UC Irvine

24 / 25