Find closest vector
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We have a list of vectors with dimensions greater than two(dimension can be 10, 32, 64 or 15) and an arbitrary vector as the input.
It is required to find from the list of vectors the closest to the input vector
(for example: 10000 and 10001 are close vectors, but 10111 and 10000 are not close vectors), but without complete passage through the list. I know several Nearest neighbor search algorithms that allow us to find the closest similar element: kd-trees, Voronoi diagram , but they are aimed at finding elements in a plane or in 3-dimensional space.
Are there any algorithms, which allow to find the nearest vector, which dimension greater then 2?
algorithm search
edited Nov 12 at 12:30
Joris Schellekens
5,95611141
asked Nov 10 at 9:51
cvxbcvbsd fsddfgdfg
82
add a comment |
up vote
-1
down vote
favorite
We have a list of vectors with dimensions greater than two(dimension can be 10, 32, 64 or 15) and an arbitrary vector as the input.
It is required to find from the list of vectors the closest to the input vector
(for example: 10000 and 10001 are close vectors, but 10111 and 10000 are not close vectors), but without complete passage through the list. I know several Nearest neighbor search algorithms that allow us to find the closest similar element: kd-trees, Voronoi diagram , but they are aimed at finding elements in a plane or in 3-dimensional space.
Are there any algorithms, which allow to find the nearest vector, which dimension greater then 2?
algorithm search
edited Nov 12 at 12:30
Joris Schellekens
5,95611141
asked Nov 10 at 9:51
cvxbcvbsd fsddfgdfg
82
Comments are not for extended discussion; this conversation has been moved to chat.
– Samuel Liew♦
Nov 10 at 20:37
add a comment |
up vote
-1
down vote
favorite
up vote
-1
down vote
favorite
We have a list of vectors with dimensions greater than two(dimension can be 10, 32, 64 or 15) and an arbitrary vector as the input.
It is required to find from the list of vectors the closest to the input vector
(for example: 10000 and 10001 are close vectors, but 10111 and 10000 are not close vectors), but without complete passage through the list. I know several Nearest neighbor search algorithms that allow us to find the closest similar element: kd-trees, Voronoi diagram , but they are aimed at finding elements in a plane or in 3-dimensional space.
Are there any algorithms, which allow to find the nearest vector, which dimension greater then 2?
algorithm search
edited Nov 12 at 12:30
Joris Schellekens
5,95611141
asked Nov 10 at 9:51
cvxbcvbsd fsddfgdfg
82
We have a list of vectors with dimensions greater than two(dimension can be 10, 32, 64 or 15) and an arbitrary vector as the input.
It is required to find from the list of vectors the closest to the input vector
(for example: 10000 and 10001 are close vectors, but 10111 and 10000 are not close vectors), but without complete passage through the list. I know several Nearest neighbor search algorithms that allow us to find the closest similar element: kd-trees, Voronoi diagram , but they are aimed at finding elements in a plane or in 3-dimensional space.
Are there any algorithms, which allow to find the nearest vector, which dimension greater then 2?
algorithm search
algorithm search
edited Nov 12 at 12:30
Joris Schellekens
5,95611141
asked Nov 10 at 9:51
cvxbcvbsd fsddfgdfg
82
edited Nov 12 at 12:30
Joris Schellekens
5,95611141
asked Nov 10 at 9:51
cvxbcvbsd fsddfgdfg
82
edited Nov 12 at 12:30
Joris Schellekens
5,95611141
edited Nov 12 at 12:30
Joris Schellekens
5,95611141
edited Nov 12 at 12:30
Joris Schellekens
5,95611141
5,95611141
asked Nov 10 at 9:51
cvxbcvbsd fsddfgdfg
82
asked Nov 10 at 9:51
cvxbcvbsd fsddfgdfg
82
asked Nov 10 at 9:51
cvxbcvbsd fsddfgdfg
82
82
Comments are not for extended discussion; this conversation has been moved to chat.
– Samuel Liew♦
Nov 10 at 20:37
add a comment |
Comments are not for extended discussion; this conversation has been moved to chat.
– Samuel Liew♦
Nov 10 at 20:37
Comments are not for extended discussion; this conversation has been moved to chat.
– Samuel Liew♦
Nov 10 at 20:37
Comments are not for extended discussion; this conversation has been moved to chat.
– Samuel Liew♦
Nov 10 at 20:37
add a comment |
1 Answer
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Almost all index structures for nearest-neighbour search support multi-dimensional data.
For example KD-Trees and R-Trees are well suited for low dimensional data ( d < 5-10). When the number of dimensions increases you run into the curse of dimensionality and most index structures degenerate (they become less selective).
Beyond something like 20 dimensions (this is just a rule of thumb and highly data distribution dependent) these traditional index structures offer no benefit over a full scan over the data. Then you can either
- try to optimize this scan (e.g., early stopping during distance calculation or VA-File)
- use approximate nearest neighbour approaches (e.g., locality sensitive hashing) that are fast but do not guarantee to return the nearest neighbour (but usually a close one)
answered Nov 12 at 12:57
SaiBot
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1 Answer
1
active
oldest
votes
1 Answer
1
active
oldest
votes
active
oldest
votes
active
oldest
votes
up vote
0
down vote
accepted
Almost all index structures for nearest-neighbour search support multi-dimensional data.
For example KD-Trees and R-Trees are well suited for low dimensional data ( d < 5-10). When the number of dimensions increases you run into the curse of dimensionality and most index structures degenerate (they become less selective).
Beyond something like 20 dimensions (this is just a rule of thumb and highly data distribution dependent) these traditional index structures offer no benefit over a full scan over the data. Then you can either
- try to optimize this scan (e.g., early stopping during distance calculation or VA-File)
- use approximate nearest neighbour approaches (e.g., locality sensitive hashing) that are fast but do not guarantee to return the nearest neighbour (but usually a close one)
answered Nov 12 at 12:57
SaiBot
1,761414
add a comment |
up vote
0
down vote
accepted
Almost all index structures for nearest-neighbour search support multi-dimensional data.
For example KD-Trees and R-Trees are well suited for low dimensional data ( d < 5-10). When the number of dimensions increases you run into the curse of dimensionality and most index structures degenerate (they become less selective).
Beyond something like 20 dimensions (this is just a rule of thumb and highly data distribution dependent) these traditional index structures offer no benefit over a full scan over the data. Then you can either
- try to optimize this scan (e.g., early stopping during distance calculation or VA-File)
- use approximate nearest neighbour approaches (e.g., locality sensitive hashing) that are fast but do not guarantee to return the nearest neighbour (but usually a close one)
answered Nov 12 at 12:57
SaiBot
1,761414
add a comment |
up vote
0
down vote
accepted
up vote
0
down vote
accepted
Almost all index structures for nearest-neighbour search support multi-dimensional data.
For example KD-Trees and R-Trees are well suited for low dimensional data ( d < 5-10). When the number of dimensions increases you run into the curse of dimensionality and most index structures degenerate (they become less selective).
Beyond something like 20 dimensions (this is just a rule of thumb and highly data distribution dependent) these traditional index structures offer no benefit over a full scan over the data. Then you can either
- try to optimize this scan (e.g., early stopping during distance calculation or VA-File)
- use approximate nearest neighbour approaches (e.g., locality sensitive hashing) that are fast but do not guarantee to return the nearest neighbour (but usually a close one)
answered Nov 12 at 12:57
SaiBot
1,761414
Almost all index structures for nearest-neighbour search support multi-dimensional data.
For example KD-Trees and R-Trees are well suited for low dimensional data ( d < 5-10). When the number of dimensions increases you run into the curse of dimensionality and most index structures degenerate (they become less selective).
Beyond something like 20 dimensions (this is just a rule of thumb and highly data distribution dependent) these traditional index structures offer no benefit over a full scan over the data. Then you can either
- try to optimize this scan (e.g., early stopping during distance calculation or VA-File)
- use approximate nearest neighbour approaches (e.g., locality sensitive hashing) that are fast but do not guarantee to return the nearest neighbour (but usually a close one)
answered Nov 12 at 12:57
SaiBot
1,761414
edited Nov 14 at 7:24
answered Nov 12 at 12:57
SaiBot
1,761414
answered Nov 12 at 12:57
SaiBot
1,761414
answered Nov 12 at 12:57
SaiBot
1,761414
1,761414
add a comment |
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– Samuel Liew♦
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