Upcoming SlideShare
×

# Sketch sort sugiyamalab-20101026 - public

1,216 views

Published on

1 Like
Statistics
Notes
• Full Name
Comment goes here.

Are you sure you want to Yes No
• Be the first to comment

Views
Total views
1,216
On SlideShare
0
From Embeds
0
Number of Embeds
308
Actions
Shares
0
4
0
Likes
1
Embeds 0
No embeds

No notes for slide

### Sketch sort sugiyamalab-20101026 - public

1. 1. Yasuo Tabei(JST Minato ERATO Project) Joint work with Takeaki Uno(NII), Masashi Sugiyama (TITECH), Koji Tsuda (AIST) Seminar@Sugiyamalab, October 26, 2010(Tue.)
2. 2.  Motivation - Development of large-scale data - Needs for all pairs similarity search method - Single sorting method and its drawbacks  Method - Multiple sorting method - Locality sensitive hashing - SketchSort  Experiments - Comparison of other state-of-the art methods - Use large-scale image datasets
3. 3.  Images - flicker, image search etc - 80 million tiny images (Torralba et al., (2008)) Genome Sequences - NCBI Sequence Read Archive - A large-scale genome sequences from various organisms Chemical Compounds - NCBI PubChem - 28 million chemical compounds
4. 4. x=(0.3, -0.3, 0.5, 1.2, …) Image Chemical Compound x=(1, 0, 1, 0, 0, 0, 1, …) Vector Text, Protein, DNA/RNA etc sift fingerprint
5. 5.  Mapping vector to binary string (sketch) - Conserve the distance in the original space x=(0.3, 0.1, 0.5, 0.6, 0.7, 1.2, -0.2,…) s=1010010001110001010…  Advantage - Can keep giga-scale data in main memory - Accelerate various algorithms Mapping
6. 6.  Finding all neighbor pairs from vector data - Given a set of data-points - Find all pairs within a distance , s.t.　ji xx , ε　Δ ),( ji xx ε
7. 7.  Can build a neighborhood graph - Vertex: a data-point - Edge : a neighbor pair Applications: semi-supervised learning, spectral clustering, ROI detection in images, retrieval of protein sequences, etc ε
8. 8.  Finding neighbor pairs by sorting  Map vector data to skeches (a)Input 1:101111 2:110101 3:110010 4:010000 5:101000 6:111100 7:000000 8:010110 9:110110 10:100100 (b) Sort 7:000000 4:010000 8:010110 10:100100 5:101000 1:101111 3:110010 2:110101 9:110110 6:111100 (c) Scan neighbors 7:000000 4:010000 8:010110 10:100100 5:101000 1:101111 3:110010 2:110101 9:110110 6:111100 w w
9. 9.  Need a large number of distance calcuration for achieving reasonable accuracy  Can not derive an analytical estimation of the fraction of missing neighbors
10. 10.  Motivation - Large-scale data - Needs for all pairs similarity search method - Single sorting method, and its drawbacks  Method - Multiple sorting method - Locality sensitive hashing - SketchSort  Experiments - Comparison of other state-of-the art methods - Use large-scale image datasets
11. 11.  Input: set of fixed-length strings S={s1,…,sn }  Output: all pairs of strings within a Hamming distance d  By appling radixsort, enumerate all pairs in O(n+m) - n: number of strings, m: output pairs  Introduce block-wise masking technique for acceleration
12. 12. Block level duplication? Check Hamming Distance? Trash Trash Yes >d No ≦d Output Enumerate all pairs which share matching block combinations by MSM Input fixed length strings s1=01001001001010100 s2=11010010010101010 s3=00010100100100100 … s6:1111 0011 1001 0111 s8:1001 0011 1001 0111
13. 13.  Sort strings by radixsort, divide strings into equivalence classes O(n)  Draw edges within all strings in an equivalece class O(m)  Computational Complexity: O(n+m) EMILY DAVID CHRIS ALICE DAVID BOBBY DAVID ALICE Sort ALICE ALICE BOBBY CHRIS DAVID DAVID DAVID EMILY Equivalence Classes
14. 14.  Mask d characters in all possible ways  Performe radixsort times  Linear time to the number of strings  Time exponential to d, polynomial to the length of strings l  Ex)d=2       d l 7:0000 0001 0011 1110 4:0100 0001 1101 1100 8:0101 1001 0111 1000 10:1001 0011 1001 0111 5:1010 0010 1110 1010 1:1011 1111 0011 1110 2:1101 0111 0111 0001 3:1100 1000 1101 1100 9:1101 1000 1101 1110 6:1111 0011 1001 0111 7:0000 0001 0011 1110 4:0100 0001 1101 1100 8:0101 1001 0111 1000 5:1010 0010 1110 1010 3:1100 1000 1101 1100 6:1111 0011 1001 0111 10:1001 0011 1001 0111 2:1101 0111 0111 0001 9:1101 1000 1101 1110 1:1011 1111 0011 1110
15. 15.  Mask d blocks in all possible ways  Can reduce the number of sorting operations  Non-neighbor might be detected  Filter out by calcurating actual Hamming distance  Ex)d=2 7:0000 0001 0011 1110 4:0100 0001 1101 1100 8:0101 1001 0111 1000 10:1001 0011 1001 0111 5:1010 0010 1110 1010 1:1011 1111 0011 1110 3:1100 1000 0000 0000 2:1101 0111 0000 0000 9:1101 1000 0000 0000 6:1111 0011 0000 0000 7:0000 0001 0011 1110 4:0100 0001 1101 1100 8:0101 1001 0111 1000 10:1001 0011 1001 0111 5:1010 0000 1110 0000 1:1011 0000 0011 0000 3:1100 0000 1101 0000 2:1101 0000 0111 0000 9:1101 0000 1101 0000 6:1111 0000 1001 0000 7:0000 0000 0000 1110 4:0100 0000 0000 1100 8:0101 0000 0000 1000 10:1001 0000 0000 0111 5:1010 0000 0000 1010 1:1011 0000 0000 1110 3:1100 0000 0000 1100 2:1101 0000 0000 0001 9:1101 0000 0000 1110 6:1111 0000 0000 0111 7:0000 0001 0011 0000 4:0000 0001 1101 0000 5:0000 0010 1110 0000 6:0000 0011 1001 0000 10:0000 0011 1001 0000 2:0000 0111 0111 0000 3:0000 1000 1101 0000 9:0000 1000 1101 0000 8:0000 1001 0111 0000 1:0000 1111 0011 0000 4:0000 0001 0000 1100 7:0000 0001 0000 1110 5:0000 0010 0000 1010 6:0000 0011 0000 0111 10:0000 0011 0000 0111 2:0000 0111 0000 0001 3:0000 1000 0000 1100 9:0000 1000 0000 1110 8:0000 1001 0000 1000 1:0000 1111 0000 1110 1:0000 0000 0011 1110 7:0000 0000 0011 1110 2:0000 0000 0111 0001 8:0000 0000 0111 1000 4:0000 0000 0111 1000 6:0000 0000 1001 0111 10:0000 0000 1001 0111 3:0000 0000 1101 1100 9:0000 0000 1101 1110 5:0000 0000 1110 1010
16. 16. Step1. Perform radixsort in a block, and detect equivalence classes Step2. For each equivalence class, perform radixsort the next block 7:0000 0001 0011 0000 4:0000 0001 1101 0000 5:0000 0010 1110 0000 6:0000 0011 1001 0000 10:0000 0011 1001 0000 2:0000 0111 0111 0000 3:0000 1000 1101 0000 9:0000 1000 1101 0000 8:0000 1001 0111 0000 1:0000 1111 0011 0000 7:0000 0001 0011 0000 4:0000 0001 1101 0000 5:0000 0010 1110 0000 6:0000 0011 1001 0000 10:0000 0011 1001 0000 2:0000 0111 0111 0000 3:0000 1000 1101 0000 9:0000 1000 1101 0000 8:0000 1001 0111 0000 1:0000 1111 0011 0000 7:0000 0001 0011 0000 4:0000 0001 1101 0000 6:0000 0011 1001 0000 10:0000 0011 1001 0000 3:0000 1000 1101 0000 9:0000 1000 1101 0000 Recursive
17. 17. 7:0000 0001 0011 1110 4:0100 0001 1101 1100 8:0101 1001 0111 1000 10:1001 0011 1001 0111 5:1010 0010 1110 1010 1:1011 1111 0011 1110 3:1100 1000 0000 0000 2:1101 0111 0000 0000 9:1101 1000 0000 0000 6:1111 0011 0000 0000 2:1101 0111 0000 0000 9:1101 1000 0000 0000 2:1101 0111 0011 0000 9:1101 1000 0000 0000 2:1101 0111 0000 0001 9:1101 1000 0000 1110 7:0000 0001 0011 0000 4:0000 0001 1101 0000 5:0000 0010 1110 0000 6:0000 0011 1001 0000 10:0000 0011 1001 0000 2:0000 0111 0111 0000 3:0000 1000 1101 0000 9:0000 1000 1101 0000 8:0000 1001 0111 0000 1:0000 1111 0011 0000 7:0000 0001 0011 0000 4:0000 0001 1101 0000 6:0000 0011 1001 0000 10:0000 0011 1001 0000 3:0000 1000 1101 0000 9:0000 1000 1101 0000 4:0000 0001 0011 1000 7:0000 0001 1101 1110 6:0000 0011 1001 0111 10:0000 0011 1001 0111 3:0000 1000 1101 1100 9:0000 1000 1101 1110 1:0000 0000 0011 1110 7:0000 0000 0011 1110 2:0000 0000 0111 0001 8:0000 0000 0111 1000 4:0000 0000 0111 1000 6:0000 0000 1001 0111 10:0000 0000 1001 0111 3:0000 0000 1101 1100 9:0000 0000 1101 1110 5:0000 0000 1110 1010 1:0000 0000 0011 1110 7:0000 0000 0011 1110 2:0000 0000 0111 0001 8:0000 0000 0111 1000 4:0000 0000 0111 1000 3:0000 0000 1101 1100 9:0000 0000 1101 1110  All neighbor pairs can be enumerated
18. 18. 7:0000 0001 0011 1110 4:0100 0001 1101 1100 8:0101 1001 0111 1000 10:1001 0011 1001 0111 5:1010 0010 1110 1010 1:1011 1111 0011 1110 3:1100 1000 0000 0000 2:1101 0111 0000 0000 9:1101 1000 0000 0000 6:1111 0011 0000 0000 2:1101 0111 0000 0000 9:1101 1000 0000 0000 2:1101 0111 0011 0000 9:1101 1000 0000 0000 2:1101 0111 0000 0001 9:1101 1000 0000 1110 7:0000 0001 0011 0000 4:0000 0001 1101 0000 5:0000 0010 1110 0000 6:0000 0011 1001 0000 10:0000 0011 1001 0000 2:0000 0111 0111 0000 3:0000 1000 1101 0000 9:0000 1000 1101 0000 8:0000 1001 0111 0000 1:0000 1111 0011 0000 7:0000 0001 0011 0000 4:0000 0001 1101 0000 6:0000 0011 1001 0000 10:0000 0011 1001 0000 3:0000 1000 1101 0000 9:0000 1000 1101 0000 4:0000 0001 0011 1000 7:0000 0001 1101 1110 6:0000 0011 1001 0111 10:0000 0011 1001 0111 3:0000 1000 1101 1100 9:0000 1000 1101 1110 1:0000 0000 0011 1110 7:0000 0000 0011 1110 2:0000 0000 0111 0001 8:0000 0000 0111 1000 4:0000 0000 0111 1000 6:0000 0000 1001 0111 10:0000 0000 1001 0111 3:0000 0000 1101 1100 9:0000 0000 1101 1110 5:0000 0000 1110 1010 1:0000 0000 0011 1110 7:0000 0000 0011 1110 2:0000 0000 0111 0001 8:0000 0000 0111 1000 4:0000 0000 0111 1000 3:0000 0000 1101 1100 9:0000 0000 1101 1110  The same pair can be detected in different block combinations Ex) (3,9), (6,10)  Naïve method takes n2 memory 6:1111 0011 1001 0111 10:1001 0011 1001 0111
19. 19. a b c d 6:1111 0011 1001 0111 10:1001 0011 1001 0111 Step1: Make a total order among blocks from left to right Step2: Make a total order among block combinations Step3: Take the minimum among matching block combinations a b c d 6:1111 0011 1001 0111 10:1001 0011 1001 0111 Combination 1 Combination 2 (a,b)<(a,c)<(a,d) (a,b)<(a,c)<(a,d) <(b,c)<(b,d)<(c,d) Combination 3 a b c d 6:1111 0011 1001 0111 10:1001 0011 1001 0111 a b c d 6:1111 0011 1001 0111 10:1001 0011 1001 0111
20. 20. If the number of blocks is k-d, Eliminate duplicate pairs, and Calculate Hamming distance Call function to equivalence classes
21. 21.  Enumerates all neighbor pairs within a distance - (xi, xj), i < j, Δ(xi,xj) ≦ε,  Basic idea - Map vector data to sketches by LSH - Enumerate all neighbor pairs by MSM  SketchSort with cosine LSH - Enumerate all neighbor pairs within a consine distance threshold ε - (xi, xj), i < j, ε≦Δ |||||||| 1),( ji j T i ji xx xx xx 
22. 22. Block level duplication? Check Hamming Distance? Chunk Level Duplication Check Cosine Distance Trash Trash Trash Trash Yes >d Yes >ε No ≦d No ≦ε Output Mapping to sketches by LSH s1=1000101001... s2=0110000101… s3=0101001000… Enumerating matching block combinations by MSM Input vector data x1=(0.1,1.2,-0.9,2.3,…) x2=(1.5,-0.1,-1.2,-1.2,…) x3=(-1.6,1.9,0.5,-0.6,…) … s6:1111 0011 1001 0111 s8:1001 0011 1001 0111
23. 23.  Basic idea - Generate a random hyperplain centered at 0 - Map each data-point to ‘1 if it is above the hyperplain, or else ‘0’  Repeat l times 10…. 1 0 1 1 0 0…. l
24. 24.  Basic idea: Map vector data to sketches and apply MSM  Not good: create long sketches and apply MSM at once  Divide long sketches to Q short sketches of length l (chunks)  Apply MSM to each chunk, obtain neighbor pairs w.r.t Hamming distance 1100101010010101 0101010101001010 101010101010011 0101010101010101 0101000101010101 010101010010100 1001010101011110 1010101010101010 101010101010100 1000001010101010 1010101010101010 101010010101011 1111001010111110 1010101011111010 010101010101010 ...... 1010101010010101 0100111101010100 001111010001011 1000010000100001 0011111101000100 010010010001111 1111000001110101 0101001010101001 001000111100101 0001010010101001 0100101011100100 101001010001000 1111000100100010 0011010100010010 010010100010001 l l MSM MSM MSM  Report neighbor pairs no more than a cosine distance threshold ε l
25. 25.  A neighbor pair of sketches can be detected in several chunks within Hamming distance d 1100101010010101 0101010101001010 101010101010011 10101111010011 111010101001001 … 0101010101010101 0101000101010101 010101010010100 11110101010011 111111111010011 … HamDist(si 1,sj 1)>d Si Sj HamDist(si 2,sj 2)≦d HamDist(si 3,sj 3)>d HamDist(si 4,sj 4)>d HamDist(si 5,sj 5)≦d Duplication!!  The same pair is outputted several times (Duplication)
26. 26. Step1: Order chunks from left to right. 1100101010010101 0101010101001010 101010101010011 10101111010011 111010101001001 … 0101010101010101 0101000101010101 010101010010100 11110101010011 111111111010011 …a 1 2 3 4 5 Step2: Check whether left chunks are no more than Hamming distance d 1100101010010101 0101010101001010 101010101010011 10101111010011 111010101001001 … 0101010101010101 0101000101010101 010101010010100 11110101010011 111111111010011 …a 1 2 3 4 5 HamDist(si 5,sj 5)≦d HamDist(si 4,sj 4)>d? HamDist(si 3,sj 3)>d? HamDist(si 2,sj 2)>d? HamDist(si 1,sj 1)>d? - If such chunk is found, trash the pair, Or else check cosine distance
27. 27.  Call function for each chunk  Check duplication four times  Divide sketches into equivalence classes  Call function recursively
28. 28.  True edges E*, Our results E  Type-I error (false positive): A non-neighbor pair has a Hamming distance within d in at least one chunk  Type II-error (false negative): A neighbor pair has a Hamming distance larger than d in all chunks
29. 29.  Basically, type-II error is more crucial - type-I errors are filtered out by distance calculations  Missing edge ratio (type-II error) is bounded as where p is an upper bound of the non-collision probability of neighbors
30. 30.  Motivation - Large-scale data - Needs for All Pairs Similarity Search Method - Single Sorting Method, and its drawbacks  Method - Multiple Sorting Method - Cosine Locality Sensitive Hashing - SketchSort  Experiments - Comparison of other state-of-the art methods - Use large-scale image datasets
31. 31.  Two image datasets - MNIST (60,000 data, 748 dimension) - Tiny Image (100,000 data, 960 dimension)  Use missing edge ratio as an evaluation measure  Set cosine distance threshold of 0.15π  Length of each chunk to 32bit  Hamming distance and number of blocks are set to (2,5) and (3,6).  Number of chunks vary from 2, 4, 6, …, 50  Compare our method to Lanczos bisection method (JMLR, 2009)
32. 32.  K-nearest neighbor graph construction by SketchSort - Keep k-nearest neighbor pairs by priority queue  Compare SketchSort to - Cover Tree (Beygelzimer et al., ICML 2006) - AllKNN (Ram et al., NIPS 2009), - Lanczos-bisection (JMLR, 2009)
33. 33.  Set parameters so as to keep missing edge ratio no more than 1.0×10-6  Enable to detect similar pairs nearly exactly  Take only 4.3 hours for 1.6 million images 0.05π 0.1π 0.15π
34. 34.  Fast all pairs similarity search method  Applicable to large-scale vector data  Applicable to Euclidean distance (Raginsky,10), Jaccard-coffecients (Broder,00)  Various applications  Software - http://code.google.com/p/sketchsort/