Dimensionality Reduction by Random Mappingpeople.csail.mit.edu/mrub/talks/random_mapping.pdf ·...

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Dimensionality Reduction by Random Mapping: Fast Similarity Computation for Clustering

Samuel KaskiHelsinki University of Technology

1998

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Outline

MotivationStandard approachesRandom mappingResults (Kaski)Heuristics Very general overview of related workConclusion

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MotivationFeature vectors

Pattern recognitionClusteringMetrics (distances), similarities

High dimensionalityImages – large windowsText – large vocabulary…

DrawbacksComputationNoiseSparse data

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Dimensionality reduction methods

Feature selectionAdapted to nature of data. E.g. text:

Stemming (going go, Tom’s Tom)Remove low frequencies

Not generally applicableFeature transformation / Multidimensional scaling

PCASVD…Computationally costly

Need for faster, generally applicable method

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Random mapping

Almost as good: Natural similarities / distances between data vectors are approx. preservedReasoning

AnalyticalEmpirical

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Related workBingham, Mannila, ’01: results of applying RP on image and text dataIndyk, Motwani ’99: use of RP for approximated NNS, a.k.a Locality-Sensitive HashingFern, Brodley ’03: RP for high dimensional data clusteringPapadimitriou ’98: LSI by random projectionDasgupta ’00: RP for learning high dimensional Gaussian mixture modelsGoel, Bebis, Nefian ’05: Face recognition experiments with random projection

Thanks to Tal Hassner

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Related work

Johnson-Lindenstrauss lemma (1984):

22 3

d

for any 0 1 and any integer n, let k be a positive integer such that

4 ln k ( ln )/ 2 / 3

Then for any set P of n points in , there is a map : such that for all p,q(1

d k

n O n

f P

ε

εε ε

ε

−

< <

≥ =−

→ ∈− 2 2 2) || || || ( ) ( ) || (1 ) || ||p q f p f q p qε− ≤ − ≤ + −

Dasgupta [3]

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Johnson-Lindenstrauss Lemma

Any n point set in Euclidian space can be embedded in suitably high (logarithmic in n, independent of d) dimension without distorting the pairwise distances by more that a factor of )1( ε±

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Random mapping methodLet Let R be dxn matrix of random values where ||ri||=1 and each rij∈R is normally i.i.d with mean 0

[ 1] [ ] [ 1]

1

dx dxn nx

n

i ii

y R x d n

r x=

= <<

= ∑

nx ∈

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Random mapping method

1

21 2

1 0 00 1 0

0 0 1

n

n

xx

x x x x

x

⎛ ⎞⎛ ⎞ ⎛ ⎞ ⎛ ⎞⎜ ⎟⎜ ⎟ ⎜ ⎟ ⎜ ⎟⎜ ⎟⎜ ⎟ ⎜ ⎟ ⎜ ⎟+ + + = =⎜ ⎟⎜ ⎟ ⎜ ⎟ ⎜ ⎟⎜ ⎟⎜ ⎟ ⎜ ⎟ ⎜ ⎟

⎝ ⎠ ⎝ ⎠ ⎝ ⎠ ⎝ ⎠

11 12 1 1

21 22 2 21 2

1 2

n

nn

d d dn d

r r r yr r r y

x x x y

r r r y

⎛ ⎞ ⎛ ⎞ ⎛ ⎞ ⎛ ⎞⎜ ⎟ ⎜ ⎟ ⎜ ⎟ ⎜ ⎟⎜ ⎟ ⎜ ⎟ ⎜ ⎟ ⎜ ⎟+ + + = =⎜ ⎟ ⎜ ⎟ ⎜ ⎟ ⎜ ⎟⎜ ⎟ ⎜ ⎟ ⎜ ⎟ ⎜ ⎟⎝ ⎠ ⎝ ⎠ ⎝ ⎠ ⎝ ⎠

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Similarity

( , ) cos|| || || ||

if || || 1,|| || 1

then cos

u vsim u vu v

u v

u v

θ

θ

⋅= =

= =

= ⋅

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Random mapping methodHow will it affect the mutual similaritiesbetween the data vectors?As R is more orthonormal the better. however R is generally not orthogonalHecht-Nielsen [4]: in a high dimensional space, there exists a much larger number of almost orthogonal than orthogonal directionsSo, R might be sufficiently good approximation for a basis

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Transformation of similarities

Similarity measure:

Properties of ε:

( , ) cos for unit vectors|| || || ||

where ,

where for and 0

T T T n

T Tij i j ii

u vsim u v u vu v

x y n R Rm n m

R R I r r i j

θ

ε ε ε

⋅= = = ⋅

= ∈

= + = ≠ =

1 1( ) ( ) ( ) ( ) 0

d dT

ij i j ik jk ik jkk k

E E r r E r r E r E rε= =

⎛ ⎞ ⎡ ⎤= = = =⎜ ⎟ ⎣ ⎦⎝ ⎠∑ ∑

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Recall

Pearson correlation coefficient

Sample correlation

Geometric interpretation

,( , )

X YX Y

Cov X Yρσ σ

=

[ ], , 2 2

( )( )ˆ

( ) ( )

i ix y x y

i i

x x y yr

x x y yρ

− −= =

− −

∑∑ ∑

cos x yx x y y

θ ⋅=

⋅ ⋅

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RecallFisher (r2z) Transformation

Variance of z is estimate of the variance of the population correlation

Let X, Y normally distributedand let r be correlation of sample of size N from X,Y

1 1log2 1

then is approximately normally distributed with1standard deviation N-3

erzr

z

+=

−

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Properties of ε:εij is an estimate of the correlation coefficient between two normally i.i.d random variables riand rj

2

11 ln is approximately normally distributed2 1

1with variance 1/ for large d-3

ij

ij

ddε

εε

σ

+

−

= ≈

as , Td R R I⇒ → ∞ →

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Statistical propertiesLet , , and assume , are normalized

( ) = (recall 0)

let =

n

T T T T T T

Tkl k l kk

k l

kl k lk l

n m n m

x y n R Rm n I m n m n mn m n m e

n m

ε εε

δ ε

≠

≠

∈

= = + = ++ =∑

∑

( ) ( ) 0kl k l k l klk l k l

E E n m n m Eδ ε ε≠ ≠

⎛ ⎞= = =⎜ ⎟

⎝ ⎠∑ ∑

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Transformation of similaritiesVariance of δ:

2 2 2( ) ( ( )) [( )( )] 0

= [ ]

[ ]

[ ] 0 only for ( and ) or ( and )

kl k l pq p qk l p q

k l p q kl pqk l p q

kl pq ki li pj qj ki li pj qji j i j

kl pq

E E E n m n m

n m n m E

E E r r r r E r r r r

E k p l q k q l p

δσ δ δ ε ε

ε ε

ε ε

ε ε

≠ ≠

≠ ≠

= − = − =

⎡ ⎤ ⎡ ⎤= =⎢ ⎥ ⎢ ⎥

⎣ ⎦ ⎣ ⎦

≠ = = = =

∑ ∑∑∑

∑ ∑ ∑∑

1 2denote ( , ), ( , )c k p l q c k q l p= = = = = =

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2 2 2 2 21 2

2 2 2

2 2 2

(corresponds to , respectively)

=

= (1 ) ( || || 1,|| || 1)

= 1

k l k l l kk l k l

k l k k l lk l k k l k

k k k k l l k kk k l

k

n m n m n m c c

n m n m n m

n m n m n m n m n m

n

δ ε ε

ε

ε

σ σ σ

σ

σ

≠ ≠

≠ ≠

= +

⎡ ⎤+⎢ ⎥

⎣ ⎦⎡ ⎤⎛ ⎞

− + − = =⎢ ⎥⎜ ⎟⎝ ⎠⎣ ⎦

−

∑ ∑

∑ ∑ ∑ ∑

∑ ∑ ∑2 2 2 2 2 2

2 2 2 2

( )

= 1+( ) 2

k k k k kk k k

k k k kk k

m n m n m

n m n m

ε

ε

σ

σ

⎡ ⎤+ −⎢ ⎥

⎣ ⎦⎡ ⎤

−⎢ ⎥⎣ ⎦

∑ ∑ ∑

∑ ∑

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That is, the distortion of the inner products as aresult of applying random mapping is 0 on averageand its variance is proportional to the inverse ofthe dimensionality of the reduced space (x 2)

2

2 2

( ) 1 by Cauchy-Schwartz ( , normalized)

2 2 /

k kk

n m n m

dδ εσ σ

≤

⇒ ≤ ≈

∑

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Sparsity of the dataSay we constrain the input vectors to have L 1’s, and say K of those occur in same position in both vectors

( ) 1

2 2 2 2 2 2 22

2 2

Now, let's normalize , and we get K corresponding

dimenstions, each with value

=[1+( ) 2 ] [1 ( ) 2( )]

1 =[1 ( ) 2( ) ]

T

k k k kk k

K Kn mLL Ln m

LK Kn m n mL L

K KL L L

δ ε ε

ε

σ σ σ

σ

−

⇒ = =

⇒ − = + −

+ −

∑ ∑

Sparser data smaller variance of error!

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Till now

1Error matrix

Expected = 0Variance proportional to 1/d

Added distortionExpected = 0Variance is O(2/d)

Behaves better on sparse data

T T Tx y n R Rm=

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Self Organizing Maps

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Self Organizing Maps

Kohonen Feature MapsUsually 1D or 2DEach map unit associated with an Rn

vectorUnsupervised, Single layer, Feed-Forward network

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SOM algorithmInitialization

RandomPattern

For each sample vector nFind winner, or BMU

Update rule:

Where hc(n),i is the neighborhood kernel and α(t) is the learning rate factor

( ),( 1) ( ) ( ) ( )[ ( )]i i c n i im t m t h t t n m tα+ = + −

( ) arg min{|| ||}ii

c n n m= −

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SOM visualization

wsom

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Back to Random Mapping

SOM should not be too sensitive to distortions by random mapping

Small neighborhoods in Rn will be mapped to small neighborhoods in Rd will probably be mapped to single MU or a set of close-by MUs

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WEBSOM document organizing system

Vector space model (Salton 1975)Vectors are histograms of words

i’th element indicates (function of) frequency of the i’th vocabulary term in the document

Direction of vector reflects doc context

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WEBSOM – example?

http://websom.hut.fi/websom/comp.ai.neural-nets-new/html/root.html

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WEBSOM – experiment setupIntput

18000 articles20 Usenet newsgroupsDifferent topic areas

VectorizingAfter removing outliers n = 5781Each word weighted entropy based

SOM768 MUsMUs labeled according to dominated group

Separability measurePercentage of articles falling into MU labeled with their own class as majority

7 experiments for each dimension

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WEBSOM - results

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heuristicsDistance metric:

= expected norm of projection of unit vector to random subspace through the origin (JL scaling term)Image data

Constructing R:Set each entry of the matrix to an i.i.d. N(0,1) valueOrthogonalize the matrix using the Gram-Schmidt algorithmNormalize the columns of the matrix to unit length

1 2 1 2|| || / || ||x x n d Rx Rx− ⇒ −/n d

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heuristics

Achlioptas [2]:Simpler distributions that are JL compatible

Only 1/3 of the operations

1 with probability 1/63 0 with probability 2/3

1 with probability 1/6ijr

+⎧⎪= ⋅⎨⎪−⎩

1 with probability 1/2 1 with probability 1/2ijr +⎧= ⎨−⎩

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RP vs. SVD - Bingham [10]

n = 50002262 newsgroup documentsRandomly chosen pairs of data vectors u, vError = uv – (Ru)(Rv)95% confidence intervals over 100 pairs of (u,v)

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RP on Mixture of Gaussians

data from a mixture of k Gaussians can be projected into O(logk) dimensions while still retaining the approximate level of separation between the clusters

Projected dimension independent of number of points and original dimensionEmpirically shown for 10lnkDecision of reduced dimension is highly studied

Dasgupta [9] – for further details!

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RP on Mixture of Gaussians

The dimension is drastically reduced while eccentric clusters remain well separated and become more spherical

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RP ensembles – Fern [7]

Experience of distorted, unstable clustering performanceDifferent runs may uncover different parts of the structure in the data that complement one another

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RP ensemblesMultiple runs of RP + EM:

Project to lower subspace dUse EM to generate a probabilistic model of a mixture of k Gaussians

Average the Pijs across n runsGenerate final clusters based on P

Can iterate of different (reduced) subspacesFern [7] - for more details!

1( | , ) ( | , )

k

ijl

P P l i P l jθ θ θ=

= ∑

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Face recognition with RP –Goal [11]

Training set: M NxN vectors (each represents a face)

Algorithm:1. compute average face:

2. Subtract mean face from each face:

3. Generate random operator R4. Project normalized faces to random subspace:

1

1 M

iiM =

Ψ = Γ∑

i iΦ = Γ − Ψ

i iw R= Φ

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Face recognition with RP

Recognition:1. Normalize2. Project to same random space3. Compare projection to database

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Face recognition with RP

Face representations need not be updated when face database changesUsing ensembles of RPs seems promisingGoel [11] – for more details!

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ConclusionsComputationally much simpler

k data vectors, d << NRP: O(dN) to build, O(dkN) to apply

If R has c nonzero entries: O(ckN) to applyPCA: O(kN2)+O(N3)

Independent of the dataHas been applied on various problems and shown satisfactory results:

Information retrievalMachine learningImage/text analysis

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Conclusions

Computation vs. PerformanceBad results?Applying Johnson-Lindenstrauss on Kaski’s setup yields k~2000 (?)

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References[1] S. Kaski. Dimensionality reduction by random mapping. In Proc. Int. Joint Conf. on Neural Networks, volume 1, pages 413–418, 1998.[2] D. Achlioptas. Database-friendly random projections. In Proc. ACM Symp. on the Principles of Database Systems, pages 274–281, 2001.[3] S. Dasgupta and A. Gupta. An elementary proof of the Johnson-Lindenstrauss lemma. Technical Report TR-99-006, International Computer Science Institute, Berkeley, California, USA, 1999.[4] R. Hecht-Nielsen. Context vectors: general purpose approximate meaning representations self-organized from raw data. In J.M. Zurada, R.J. Marks II, and C.J. Robinson, editors, Computational Intelligence: Imitating Life, pages 43–56. IEEE Press, 1994.[5] P. Indyk and R. Motwani. Approximate nearest neighbors: Towards removing the curse of dimensionality. In Proc. of 30th STOC, 1998 [6] D. Fradkin and D. Madigan. Experiments with random projections for machine learning. In KDD '03: Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining, pages 517--522, 2003

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References[7] Xiaoli Z. Fern and Carla E. Brodley, "Cluster ensembles for high dimensional data clustering: An empirical study", Techenical report CS06-30-02[8] C.H. Papadimitriou, P. Raghvan, H. Tamaki and S. Vempala, “Latent semantic analysis: A probabilistic analysis,” in Proceedings of 17th ACM Symp. On the principles of Database Systems, pp. 159–168, 1998.[9] Sanjoy Dasgupta, Experiments with Random Projection, Proceedings of the 16th Conference on Uncertainty in Artificial Intelligence, p.143-151, June 30-July 03, 2000[10] E. Bingham and H. Mannila, “Random projection in dimensionality reduction: applications to image and text data,” in Proceedings of the 7th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 245–250, 2001[11] N. Goel, G. Bebis, and A. Nefian. Face recognition experiments with random projection. In Proceedings SPIE Vol. 5779, pages 426--437, 2005

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