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# Approximate nearest neighbor for

ℓ. p. –spaces (2<p<∞) via . embeddings. Yair. . Bartal. . Lee-Ad Gottlieb Hebrew U. Ariel University. Nearest neighbor search. Problem definition:. Given a set of points S, preprocess S so that the following query can be answered efficiently:.

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## Presentation on theme: "Approximate nearest neighbor for"— Presentation transcript:

Slide1

Approximate nearest neighbor for ℓp–spaces (2<p<∞) via embeddings

Yair Bartal Lee-Ad Gottlieb Hebrew U. Ariel University

Slide2

Nearest neighbor searchProblem definition:

Given a set of points S, preprocess S so that the following query can be answered efficiently:Exact: NNS - Given query point q, what is the closest point to q in S?Approximate: ANN - Given query point q,

find a point x in S whose distance from q is within some approximation factor of the closest ?polynomial

space, polylog queryo(log n) approximation

q

Slide3

Approx. nearest neighbor searchNot possible in general metrics

More restrictive spaces?Good news! Euclidean spaceNormed spaces

Slide4

Lp Normed SpacesNorms: Recall that for d-dimensional vectors

x,y ǁx-yǁp = (|x1-y1|p+…+|

xd-yd|p

)1/p

l

1l2l∞

Slide5

Approx. Nearest neighbor searchAn

efficient ANN structure featurespolynomial space polylog query time o(log n), o(d) approximation

Efficient ANN structures exist for

Euclidean (p=2): (1+ɛ)-ANN [IM’98, KOR ‘98] Reduce dimension via JL, brute force in lower dimension.

ℓ∞: O(log log d)-ANN [Indyk

‘98] What about other norms?1≤p<2: (1+ɛ)-ANN same as Euclidean 2<p<∞: ? (previous – Andoni) subject of this paper

Slide6

Summary of resultsCombine two algorithms

for ℓp (2<p<∞):Andoni: O(log log d (logdn)1/p

) -ANNNew result: 2O(p) –ANN

Analysis: Equality at p = (

logloglog d) + (loglogdn)1/2Worse case approximation:

(loglogd) exp((loglogdn)1/2)Andoni better for larger values, New for smallerImproved bounds in metrics of low doubling dimension

Slide7

EmbeddingsAn embedding

of set X into Y with distortion D is a mapping f : X → Y such that for all x, y ∈ X:1 ≤ c・d

Y(f(x),f(y)) / dX(

x,y) ≤ Dwhere c is any scaling constant

Relaxed: one side may be preserved with constant probabilityIf an embedding is non-expansive

and has small contraction:The nearest neighbor stays close, and far points are still relatively farIf an embedding is non-contractive and has small expansion:The nearest neighbor is only a little bit farther away, and far points remain farqq

Slide8

Andoni’s algorithmBasic idea: [Andoni

‘09]Embed ℓp space into ℓ∞Run Indyk’s ANN algorithm for ℓ∞Embedding using

Frechet random variablesmax-stable distribution…

Slide9

Frechet distributionFor random variable X ∼

Frechet Pr[X < x] = e-x-p for x>0 Max-stability:Let random variables

X and Z1, . . . ,Zd

be ∼ Frechetlet v = (v1, . . . ,

vd) be a non-negative valued vector. Then the random variable

Y := maxi viZi is distributed as ǁvǁp ・X (Y ∼ ǁvǁp・X).

Slide10

Frechet distributionProof of Max-stability:

Recall Y := maxi viZi Pr[Y ≤ x] =

Pr[maxi viZi

≤ x] = Πi Pr[

viZi ≤ x] =

Πi Pr[Zi ≤ x/vi] = Πi e−(vi/x)p = e−(∑ivip)/xp = e−(ǁvǁp/x)pSimilarly, Pr[ǁvǁp・X ≤ x] = Pr[X ≤ x/ǁvǁp] = e−(ǁvǁp /x)p

Slide11

Review of Andoni’s embedding

Define embedding fb : V → ℓ∞ (b > 0): Draw Frechet

random variables Z1, . . . ,Zd. fb

maps v= (v1, . . . ,

vd) to (v1bZ1

, . . . ,vdbZd)The resulting set is V′ ∈ ℓ∞.Theorem: Set b = (3 ln n)1/p. Then fb satisfiesNon-contractive (for all points) with prob. > 1−1/nExpansion: For any u,w∈ V, with constant prob. ǁfb(u) − fb (w)ǁ∞ ≤ b ǁu − wǁp Expansion guarantee needed for only one inter-point distance: between the query point and nearest neighbor.

Slide12

Analysis of Andoni’s embedding

Theorem: Set b = (3 ln n)1/p. Then fb satisfies

Non-contractive (all points) with prob. > 1−1/nExpansion: For any u,w∈ V with constant prob.

ǁfb(u

) − fb (w) ǁ

∞ ≤ b ǁu − wǁp Proof of contraction: Take v with ǁvǁp = 1. By max-stability, ǁfb(v)ǁ∞ ∼ bǁvǁp・X = b・X, By definition of Frechet distribution, Pr[ǁfb (v)ǁ∞ < 1] = Pr[b ・X < 1] = e−(1/b)−p = n-3 . Since the embedding is linear, v may be taken to be any inter-point distance between two vectors in V, so the probability that any of the n2 inter-point distances decreases is less than n2・ n-3 = 1/n. Proof of expansion:Same approach, Pr[ǁfb(v)ǁ∞ ≤ b] = Pr[b ・X < b] = Pr[X < 1] = e−1So expansion bounded by b.

Slide13

SummaryEmbed ℓp space into ℓ

∞distortion O(b) = O(ln n)1/p Run Indyk’s ANN algorithm for ℓ∞O(loglogd

)-ANNFinal guaranteeO(loglogd

ln1/pn)-ANN

Slide14

An improvementWe can improve the guarantees of Andoni’s algorithm by considering the

doubling dimension of the space.Doubling constant: number of half-radius balls necessary to cover big ball.Doubling dimension: log(doubling constant)For example, d-dimensional Euclidean space has doubling dimension Ѳ(d)

1

2

3

46578

Slide15

Improvement outlineNearest neighbor search can be reduced to a series of subproblems

Searches on spaces with small aspect ratioSo we can take a net on the subspaces, and run Andoni’s algorithm on the nets insteadSize of net: ddimO(ddim

)Approximation:Andoni: O(log

log d (logdn)1/p)

Improved: O(log log d

(ddim logdddim)1/p)

Slide16

New algorithmBasic idea: Embed ℓp

space into ℓ2Run ANN algorithm for ℓ2Embedding using the Mazur map

Slide17

Mazur mapMazur map is a mapping from ℓp

to ℓq, for any 0 < p, q < ∞. The mapping of vector v ∈ ℓp is defined as M(v) = (|v

0|p/q, |v1|p/q

, . . . , |vm−1|p/q

)For set V, let C satisfy C ≥ ǁvǁp, for all ∈

V. Our embedding f is the Mazur map from ℓp to ℓ2, scaled down by a factor (p/2) C p/2 – 1f is non-expansive. Contraction: If ǁx − yǁp = u, then ǁf(x) − f(y)ǁp ≥ 2p-1 (2C)1−p/2 up/2 [Binyamini & Lindenstrauss ‘00]

Slide18

ANN via the Mazur mapThe distortion of our embedding is large depends on the diameter C of the space:

2p-1 (2C)1−p/2up/2But we can show that this guarantee is sufficient to solve a specific case of nearest neighbor in ℓ

p: the c-bounded nearest neighbor problem.

Slide19

C-bounded nearest neighborDefine the c-bounded near neighbor problem where c ≥

ǁvǁp for all v ∈ VIf there is a point in V within distance 1 of query q, return it or some point in V within distance c/9

of q. This is a c/9-ANN.If there is no point in V within distance 1 of query q, return null

or some point in V within distance c/9 of q.

Slide20

C-bounded nearest neighborApproximately solve the c-bounded nearest neighbor problem in ℓp

, for c=p18p/2Embed from ℓp to ℓ2Compute a 2-ANN in ℓ2

Analysis: the Mazur map ensures that inter-point distances of c/9

or greater map to at least 2p-1 (2c) 1

−p/2 (c/9) p/2 = 4. If

q possesses a neighbor in the original space at distance 1 or less, the 2-ANN finds a neighbor at distance 2 in the embedded space and less than c/9 in the origin space.qq

Slide21

C-bounded nearest neighborWe can show that the C-bounded nearest neighbor problem can be used to give a c-ANN for the regular (unbound problem).

Final result: 2O(p)-ANN

Slide22

ConclusionCombine two algorithms

for ℓp (2<p<∞):Andoni: O(log log d (logdn)1/p

) -ANNNew result: 2O(p) –ANN

Worse case approximation:

(loglogd) exp

((loglogdn)1/2)Improved bounds in metrics of low doubling dimension