similarity search in high dimensions via hashing (original lsh with hamming distance)
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8/3/2019 Similarity Search in High Dimensions via Hashing (Original LSH With Hamming Distance)
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0 1000 2000 3000 4000 5000 6000 7000 80000
0.2
0.4
0.6
0.8
1
Interpoint distance
Normalized
frequency
Point set distance distribution
0 100 200 300 400 500 600 7000
0.2
0.4
0.6
0.8
1
Normalizedfrequency
Interpoint distance
Texture data set point distribution
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1 2 3 4 5 6 7 8 9 100
0.1
0.2
0.3
0.4
0.5
0.6
Number of indices
Error
alpha=2, n=19000, d=64, k=700
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0.1 0.2 0.5 1 1.9
x 104
0
5
10
15
20
Number of database points
Diskacc
esses
alpha = 2, 1NNS
SRTree
LSH, error=.02
LSH, error=.05
LSH, error=.1
LSH, error=.2
0.1 0.2 0.5 1 1.9
x 104
0
5
10
15
20
25
30
35
40
Number of database points
Diskaccesses
alpha = 2, 10NNS
SRTree
LSH, error=.02
LSH, error=.05
LSH, error=.1
LSH, error=.2
0.1 0.2 0.5 1 1.9
x 104
0
0.05
0.1
0.15
0.2
0.25
Number of database points
Miss
ratio
alpha=2, n=19000, d=64, 1NNS
Error=.05Error=.1
0.10.2 0.5 1 1.9
x 104
0
0.1
0.2
0.3
0.4
0.5
Number of database points
Missratio
alpha=2, n=19000, d=64, 10NNS
Error=.05Error=.1
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8 27 640
2
4
6
8
10
12
14
16
18
20
Dimensions
Diskacc
esses
alpha = 2, 1NNS
SRTree
LSH, error=.02
LSH, error=.05
LSH, error=.1
LSH, error=.2
8 27 640
5
10
15
20
25
30
35
Dimensions
Diskaccesses
alpha = 2, 10NNS
SRTree
LSH, error=.02
LSH, error=.05
LSH, error=.1
LSH, error=.2
1 10 20 50 1000
5
10
15
Number of nearest neighbors
Diskacc
esses
alpha=2, n=19000, d=64
Error=.05
Error=.1
Error=.2
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10 15 20 25 30 35 40 45 500
50
100
150
200
250
300
350
400
450
Error (%)
Diskaccesses
Performance vs error
SRTreeLSH
0 50 100 150 2000
200
400
600
800
1000
1200
1400
NumberofDiskAcces
ses
Data Set Size
SRTreeLSH
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