Lower Bounds for NNS and Metric Expansion

1. Lower Bounds for NNS and Metric Expansion RinaPanigrahy KunalTalwar UdiWieder Microsoft Research SVC TexPoint fonts used in EMF. Read the TexPoint manual before you delete this box.: AA

2. Nearest Neighbor Search Given points in a metric space Preprocess into a small data structure Given a query point Quickly retrieve the closest to Many Applications

3. Decision Version. Given search radius r • Find a point in distance r of query point • Relation to Approximate NNS: • If second neighbor is at distance cr • Then this is also a c-approximate NN r cr

4. Cell Probe Model Preprocess into data structure with • words • bits per word Query algorithm gets charged t if it probes words of • All computation is free Study tradeoff between and In this talk w m

5. Many different lower bounds n.exp(ϵ3 d)

6. Lower bounds from Expansion Show a unified approach for proving cell probe lower bounds for near neighbor and other similar problems. Show that all lower bounds stem from the same combinatorial property of the metric space Expansion : |number of points near A|/|A| (show some new lower bounds)

7. Graphical Nearest Neighbor • Convert metric space to Graph • Place an edge if nodes are within distance r • Return a neighbor of the query. Now r=1

8. Graphical Nearest Neighbor • Assume uniform degree • Use a random data set • Assume W.h.p the n balls are disjoint.

9. Deterministic Bounds via Expansion

10. Deterministic Bound • sdddddddddddddddlklkj

11. Example Application n.exp(ϵ2d)

12. Proof Idea when t=1 Shattering • F : V → [m] partitions V into m regions • Split large regions • A random ball is shattered into many parts: about ф(G) • ф(G) replication in space

13. Proof Idea when t=1 • determines which cell in is read • Select a fraction of cells such • it is likely that cantains a quarter of the data set points • So, and

14. Generalizing for larger t • Select a fraction of each table such • Continue as before • Non adaptive algorithms • Adaptive alg. depend upon content of selected cells • Subexp. number of algs • Union bound

15. Randomized Bounds • So far we assumed the algorithm is correct on • What if only of are good query point? Need to relax the definition of vertex expansion

16. Randomized Bounds • Robust Expansion • N(A) captures all edges from A • Expansion =|N(A)|/|A| • Capture only ¾ of the edges from A A N(A)

17. Robust Exapnsion • Small set vertex expansion: • In other words:We can cover all the edges incident on with a set of size • We can cover of the edges incident on with a set of size • Robust expansion is at least the edge expansion

18. Bound for Randomized Data Structure • Theorem: if is weakly Independent, then a randomized data structure that answers GNS queries with space and queries must satisfy and

19. Proof Idea when t=1 Shattering • Most of a random ball is shattered into many parts: about фr • фr replication in space

20. Generalizing for larger t • Sample 1/фr1/t fraction from each table. • A random ball, good part survives in all tables. • Union bound for adaptive is trickier.

21. Applications • We know how to calculate robust expansion of graphs derived from: • when (known) • when (new) • when (natural input dist.) • Don’t know the robust expansion of: • when

22. General Upper Bound • Say is a Cayley Graph • Take • Take with r.e. • Use random translations of to define the access function • For rand. input success prob. is constant

23. Conclusions and Open Problems Unified approach to NNS cell probe lower bounds • often characterized by expansion • Average case with natural distributions • Higher lower bounds? • Improve dependency on (very hard) • Dynamic NNS, tight bound for special cases shown in the paper

25. Approximate Near Neighbor Search • sdfsdfsffjlaskdjffj

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27. Graphical Nearest Neighbor

29. Randomized Bounds • So far we assumed the algorithm is correct on • What if only of are good query point? Need to relax the definition of vertex expansion and independence is weakly independent if for random it holds that

30. Deterministic Bounds via Expansion

31. Proof Idea • Can we plug the new definitions in the old proof? • Conceptually – yes! • Actually….well no • Dependencies everywhere – the set of good neighbors of a data point depends upon the rest of the data set • Solving this is the technical crux of the paper