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# On the Impossibility of Dimension Reduction for Doubling Subsets of L p - PowerPoint PPT Presentation

On the Impossibility of Dimension Reduction for Doubling Subsets of L p. Yair Bartal Lee-Ad Gottlieb Ofer Neiman. Embedding and Distortion. L p spaces: L p k is the metric space Let ( X,d ) be a finite metric space A map f:X → L p k is called an embedding

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### On the Impossibility of Dimension Reduction for Doubling Subsetsof Lp

YairBartal

Ofer Neiman

Embedding and Distortion Subsets

• Lp spaces: Lpk is the metric space

• Let (X,d) be a finite metric space

• A map f:X→Lpk is called an embedding

• The embedding is non-expansive and has distortion D, if for all x,yϵX :

JL Lemma Subsets

• Lemma: Any n points in L2 can be embedded into L2k, k=O((log n)/ε2) with 1+ε distortion

• Extremely useful for many applications:

• Machine learning

• Compressive sensing

• Nearest Neighbor search

• Many others…

• Limitations: specific to L2, dimension depends on n

• There are lower bounds for dimension reduction in L1, L∞

• For general n-point sets in Lp, Ω(logDn) dimensions are required for distortion D (volume argument)

• BC’03 (and also LN’04, ACNN’11, R’12) showed strong impossibility results in L1

• The dimension must be for distortion D

Doubling Dimension Subsets

• Doubling constant: The minimal λ so that every ball of radius 2r can be covered by λ balls of radius r

• Doubling dimension: log2λ

• A measure for dimensionality of a metric space

• Generalizes the dimension for normed space: Lpk has doubling dimension Θ(k)

• The volume argument holds only for metrics with high doubling dimension

Overcoming the Lower Bounds? Subsets

• One could hope for an analogous version of the JL-Lemma for doubling subsets

• Question: Does every set of points in L2 of constant doubling dimension, embeds to constant dimensional space with constant distortion?

• More ambitiously: Any subset of L2 with doubling constant λ, can be embedded into L2k, k=O((log λ)/ε2) with 1+ε distortion

Our Result Subsets

• Such a dimension reduction is impossible in the Lp spaces with p>2

• Thm: For any p>2 there is a constant c, such that for any n, there is a subset A of Lp of size n with doubling constant O(1), and any embedding of A into Lpkwith distortion at most D satisﬁes

Our Result Subsets

• Thm: For any p>2 there is a constant c, such that for any n, there is a subset A of Lp of size n with doubling constant O(1), and any embedding of A into Lpkwith distortion at most D satisﬁes

• Note: any sub-logarithmic dimension requires non-constant distortion

• We also show a similar bound for embedding from Lp into Lq, for all q≠2

• Lafforgue and Naor concurrently proved this using analytic tools, and their counterexample is based on the Heisenberg group

Implications Subsets

• Rules out a class of algorithms for NN-search, clustering, routing etc.

• The first non-trivial result on non-linear dimension reduction for Lp with p≠1,2,∞

• Comment: For p=1, there is a stronger lower bound for doubling subsets, the dimension of any embedding with distortion D (into L1) must be at least (LMN’05)

The SubsetsLaakso Graph

G0

G1

• A recursive graph, Gi+1 is obtained from Gi by replacing every edge with a copy of G1

• A series-parallel graph

• Has doubling constant 6

G2

Simple Case: p= Subsets∞

• The Laakso graph lies in high dimensional L∞

• Assume w.l.o.g that there is a non-expansive embedding f with distortion D into L∞k

• Proof idea:

• At each step, find an edge whose L2stretch is increased by some value, compared to the stretch of its parent edge

• When stretch(u,v) > k, we will have a contradiction, as

Simple Case: p= Subsets∞

u

• Consider a single iteration

• The pair a,b is an edge of the previous iteration

• Let fj be the j-th coordinate

• There is a natural embedding that does not increase stretch...

• But then u,v may be distorted

s

a

b

t

v

fj(a)

fj(b)

Simple Case: p=∞ Subsets

u

• For simplicity (and w.l.o.g) assume

• fj(s)=(fj(b)-fj(a))/4

• fj(t)=3(fj(b)-fj(a))/4

• fj(v)=(fj(b)-fj(a))/2

• Let Δj(u) be the difference between fj(u) and fj(v)

• The distortion D requirement imposes that for some j, Δj(u)>1/D (normalizing so that d(u,v)=1)

s

a

b

t

v

fj(a)

fj(b)

Δj(u)

Simple Case: p=∞ Subsets

u

s

a

b

t

• The stretch of u,s will increase due to the j-th coordinate

• But may decrease due toother coordinates..

• Need to prove that for one of the pairs {u,s}, {u,t}, the total L2 stretch increases by at least

• Compared to the stretch of a,b

v

fj(a)

fj(b)

Δj(u)

u

s

a

b

t

v

fh(a)

fh(b)

-Δh(u)

Simple Case: p=∞ Subsets

u

s

a

b

t

• Observe that in the j-thcoordinate:

• If the distance between u,s increases by Δj(u),

• Then the distance between u,t decreases by Δj(u) (and vise versa)

• Denote by x the stretch of a,b in coordinate j

• The average of the L2stretch of {u,s} and {u,t} (in the j-th coordinate alone) is:

v

fj(a)

fj(b)

Δj(u)

Simple Case: p=∞ Subsets

• For one of the pairs {u,s}, {u,t}, the total L2 stretch (over all coordinates) increases by

• Continue with this edge

• The number of iterations must be at mostkD2(otherwise the stretch will begreater than k)

• But # of iterations ≈ log n

• Finally,

u

s

a

t

b

v

Going Beyond Infinity Subsets

• For p<∞, we cannot use the Laakso graph

• Requires high distortion to embed it into Lp

• Instead, we build an instance in Lp, inspired by the Laakso graph

• The new points u,v will use a new dimension

• Parameter ε determines the (scaled) u,v distance

u

b

a

s

t

ε

v

Going Beyond Infinity Subsets

• Problem: the u,s distance is now larger than 1, roughly 1+εp

• Causes a loss of ≈ εp in the stretch of each level

• Since u,v are at distance ε, the increase to the stretch is now only (ε/D)2

• When p>2, there is a choice of ε for which the increase overcomes the loss

u

b

a

s

t

ε

v

Conclusion Subsets

• We show a strong lower bound against dimension reduction for doubling subsets of Lp, for any p>2

• Can our techniques be extended to 1<p<2 ?

• The u,s distance when p<2 is quite large, ≈ 1+(p-1)ε2 , so a different approach is required

• General doubling metrics embed to Lp with distortion O(log1/pn) (for p≥2)

• Can this distortion bound be obtained in constant dimension?