information retrieval through various approximate matrix decompositions

1

Information Retrieval through Various Approximate Matrix

Decompositions

Kathryn Linehan

Advisor: Dr. Dianne O’Leary

2

Querying a Document Database

We want to return documents that are relevant to entered search terms

Given data: • Term-Document Matrix, A

• Entry ( i , j ): importance of term i in document j

• Query Vector, q• Entry ( i ): importance of term i in the query

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Solutions

Literal Term Matching• Compute score vector: s = qTA

• Return the highest scoring documents

• May not return relevant documents that do not contain the exact query terms

Latent Semantic Indexing (LSI)• Same process as above, but use an approximation

to A

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Term-Document Matrix Approximation

Standard approximation used in LSI: rank-k SVD

Project Goal: evaluate use of term-document matrix approximations other than rank-k SVD in LSI• Nonnegative Matrix Factorization (NMF)

• CUR Decomposition

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Matrix Approximation Validation

Let be an approximation to A As the rank of increases, we expect the

relative error, , to go to zero Matrix approximation can be applied to any

matrix A• Preliminary test matrix A: 50 x 30 random sparse

matrix

• Future test matrices: three large sparse term-document matrices

A~

FFAAA

~

A~

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Nonnegative Matrix Factorization (NMF)

Term-document matrix is nonnegative

HWA *

m x n m x k

k x n

• W and H are nonnegative

• kWHrank )(

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NMF

Multiplicative update algorithm of Lee and Seung found in [1]• Find W, H to minimize

• Random initialization for W,H

• Convergence is not guaranteed, but in practice it is very common

• Slow due to matrix multiplications in iteration

2

2

1FWHA

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NMF Validation

A: 50 x 30 random sparse matrix. Average over 5 runs.

5 10 15 20 25 300

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8NMF Validation: Relative Error

k

rela

tive

erro

r

5 10 15 20 25 300

2

4

6

8

10

12NMF Validation: Run Time

k

run

time

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CUR Decomposition

Term-document matrix is sparse

**A C U R

m x n m x c

c x r r x n

• C (R) holds c (r) sampled and rescaled columns (rows) of A

• U is computed using C and R

• kCURrank )(where k is a rank parameter

,

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CUR Implementations

CUR algorithm in [2] by Drineas, Kannan, and Mahoney• Linear time algorithm

• Modification: use ideas in [3] by Drineas, Mahoney, Muthukrishnan (no longer linear time)

• Improvement: Compact Matrix Decomposition (CMD) in [5] by Sun, Xie, Zhang, and Faloutsos

• Other Modifications: our ideas

Deterministic CUR code by G. W. Stewart

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Sampling Column (Row) norm sampling [2]

• Prob(col j) = (similar for row i)

Subspace Sampling [3]• Uses rank-k SVD of A for column probabilities

• Prob(col j) =

• Uses “economy size” SVD of C for row probabilities

• Prob(row i) =

Sampling without replacement

22)(:, FAjA

kjV kA2

, :),(

ciUC2:),(

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Sampling Comparison

A: 50 x 30 random sparse matrix. Average over 5 runs.Legend: Sampling,U,Scaling (Scaling only for without replacement sampling)

5 10 15 20 25 300

0.005

0.01

0.015

0.02

0.025

0.03

0.035

0.04CUR Validation: Run Time

k

run

time

CN,L

S,Lw/o R,L,w/o Sc

w/o R,L,Sc

5 10 15 20 25 300

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1CUR Validation: Relative Error

k

rela

tive

erro

r

CN,L

S,Lw/o R,L,w/o Sc

w/o R,L,Sc

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Computation of U

Linear algorithm U: approximately solves

, where [2]

Optimal U: solves

FU

UCA ˆminˆ

URU ˆ

2min F

U

CURA

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U Comparison

A: 50 x 30 random sparse matrix. Average over 5 runs. Legend: Sampling,U

5 10 15 20 25 300

0.01

0.02

0.03

0.04

0.05

0.06

0.07

0.08CUR Validation: Run Time

k

run

time

CN,L

CN,OS,L

S,O

5 10 15 20 25 30

0.4

0.5

0.6

0.7

0.8

0.9

1CUR Validation: Relative Error

k

rela

tive

erro

r

CN,L

CN,OS,L

S,O

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Compact Matrix Decomposition (CMD) Improvement

Remove repeated columns (rows) in C (R) Decreases storage while still achieving the

same relative error [5]

Algorithm [2] [2] with CMD

Runtime 0.008060 0.007153

Storage 880.5 550.5

Relative Error 0.820035 0.820035

A: 50 x 30 random sparse matrix, k = 15. Average over 10 runs.

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Deterministic CUR

Code by G. W. Stewart Uses a RRQR algorithm that does not

store Q• We only need the permutation vector

• Gives us the columns (rows) for C (R)

Uses optimal U

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CUR Comparison

A: 50 x 30 random sparse matrix. Average over 5 runs.Legend: Sampling,U,Scaling (Scaling only for without replacement sampling)

5 10 15 20 25 300

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1CUR Validation: Relative Error

k

rela

tive

erro

r

5 10 15 20 25 300

0.01

0.02

0.03

0.04

0.05

0.06

0.07

0.08CUR Validation: Run Time

k

run

time

CN,LCN,O

S,L

S,O

w/o R,L,w/o Sc

w/o R,L,ScD

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Future Project Goals

Finish investigation of CUR improvement Validate NMF and CUR using term-document

matrices Investigate storage, computation time and

relative error of NMF and CUR Test performance of NMF and CUR in LSI

• Use average precision and recall, where the average is taken over all queries in the data set

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Precision and Recall

Measurements of performance for document retrieval Let Retrieved = number of documents retrieved,

Relevant = total number of relevant documents to the query, RetRel = number of documents retrieved that are relevant.

Precision:

Recall:

Retrieved

RetRel)Retrieved( P

Relevant

RetRel)Retrieved( R

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Further Topics

Time permitting investigations• Parallel implementations of matrix

approximations

• Testing performance of matrix approximations in forming a multidocument summary

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ReferencesMichael W. Berry, Murray Browne, Amy N. Langville, V. Paul Pauca, and Robert J. Plemmons. Algorithms and applications for approximate nonnegative matrix factorization. Computational Statistics and Data Analysis, 52(1):155-173, September 2007.

Petros Drineas, Ravi Kannan, and Michael W. Mahoney. Fast Monte Carlo algorithms for matrices iii: Computing a compressed approximate matrix decomposition. SIAM Journal on Computing, 36(1):184-206, 2006.

Petros Drineas, Michael W. Mahoney, and S. Muthukrishnan. Relative-error CUR matrix decompositions. SIAM Journal on Matrix Analysis and Applications, 30(2):844-881, 2008.

Tamara G. Kolda and Dianne P. O'Leary. A semidiscrete matrix decomposition for latent semantic indexing in information retrieval. ACM Transactions on Information Systems, 16(4):322-346, October 1998.

Jimeng Sun, Yinglian Xie, Hui Zhang, and Christos Faloutsos. Less is more: Sparse graph mining with compact matrix decomposition. Statistical Analysis and Data Mining, 1(1):6-22, February 2008.

[3]

[2]

[1]

[4]

[5]