Re: Build k-NN graph for large dataset
Yes you need to use dimensionality reduction and/or locality sensitive hashing to reduce number of pairs to compare. There is also LSH implementation for collection of vectors I have just published here: https://github.com/marufaytekin/lsh-spark. Implementation i based on this paper: http://www.cs.princeton.edu/courses/archive/spr04/cos598B/bib/CharikarEstim.pdf I hope It will help. Maruf On Thu, Aug 27, 2015 at 9:16 AM, Jaonary Rabarisoa jaon...@gmail.com wrote: Thank you all for these links. I'll check them. On Wed, Aug 26, 2015 at 5:05 PM, Charlie Hack charles.t.h...@gmail.com wrote: +1 to all of the above esp. Dimensionality reduction and locality sensitive hashing / min hashing. There's also an algorithm implemented in MLlib called DIMSUM which was developed at Twitter for this purpose. I've been meaning to try it and would be interested to hear about results you get. https://blog.twitter.com/2014/all-pairs-similarity-via-dimsum Charlie — Sent from Mailbox On Wednesday, Aug 26, 2015 at 09:57, Michael Malak michaelma...@yahoo.com.invalid, wrote: Yes. And a paper that describes using grids (actually varying grids) is http://research.microsoft.com/en-us/um/people/jingdw/pubs%5CCVPR12-GraphConstruction.pdf In the Spark GraphX In Action book that Robin East and I are writing, we implement a drastically simplified version of this in chapter 7, which should become available in the MEAP mid-September. http://www.manning.com/books/spark-graphx-in-action -- If you don't want to compute all N^2 similarities, you need to implement some kind of blocking first. For example, LSH (locally sensitive hashing). A quick search gave this link to a Spark implementation: http://stackoverflow.com/questions/2771/spark-implementation-for-locality-sensitive-hashing On Wed, Aug 26, 2015 at 7:35 AM, Jaonary Rabarisoa jaon...@gmail.com wrote: Dear all, I'm trying to find an efficient way to build a k-NN graph for a large dataset. Precisely, I have a large set of high dimensional vector (say d 1) and I want to build a graph where those high dimensional points are the vertices and each one is linked to the k-nearest neighbor based on some kind similarity defined on the vertex spaces. My problem is to implement an efficient algorithm to compute the weight matrix of the graph. I need to compute a N*N similarities and the only way I know is to use cartesian operation follow by map operation on RDD. But, this is very slow when the N is large. Is there a more cleaver way to do this for an arbitrary similarity function ? Cheers, Jao
Re: Build k-NN graph for large dataset
Thank you all for these links. I'll check them. On Wed, Aug 26, 2015 at 5:05 PM, Charlie Hack charles.t.h...@gmail.com wrote: +1 to all of the above esp. Dimensionality reduction and locality sensitive hashing / min hashing. There's also an algorithm implemented in MLlib called DIMSUM which was developed at Twitter for this purpose. I've been meaning to try it and would be interested to hear about results you get. https://blog.twitter.com/2014/all-pairs-similarity-via-dimsum Charlie — Sent from Mailbox On Wednesday, Aug 26, 2015 at 09:57, Michael Malak michaelma...@yahoo.com.invalid, wrote: Yes. And a paper that describes using grids (actually varying grids) is http://research.microsoft.com/en-us/um/people/jingdw/pubs%5CCVPR12-GraphConstruction.pdf In the Spark GraphX In Action book that Robin East and I are writing, we implement a drastically simplified version of this in chapter 7, which should become available in the MEAP mid-September. http://www.manning.com/books/spark-graphx-in-action -- If you don't want to compute all N^2 similarities, you need to implement some kind of blocking first. For example, LSH (locally sensitive hashing). A quick search gave this link to a Spark implementation: http://stackoverflow.com/questions/2771/spark-implementation-for-locality-sensitive-hashing On Wed, Aug 26, 2015 at 7:35 AM, Jaonary Rabarisoa jaon...@gmail.com wrote: Dear all, I'm trying to find an efficient way to build a k-NN graph for a large dataset. Precisely, I have a large set of high dimensional vector (say d 1) and I want to build a graph where those high dimensional points are the vertices and each one is linked to the k-nearest neighbor based on some kind similarity defined on the vertex spaces. My problem is to implement an efficient algorithm to compute the weight matrix of the graph. I need to compute a N*N similarities and the only way I know is to use cartesian operation follow by map operation on RDD. But, this is very slow when the N is large. Is there a more cleaver way to do this for an arbitrary similarity function ? Cheers, Jao
Build k-NN graph for large dataset
Dear all, I'm trying to find an efficient way to build a k-NN graph for a large dataset. Precisely, I have a large set of high dimensional vector (say d 1) and I want to build a graph where those high dimensional points are the vertices and each one is linked to the k-nearest neighbor based on some kind similarity defined on the vertex spaces. My problem is to implement an efficient algorithm to compute the weight matrix of the graph. I need to compute a N*N similarities and the only way I know is to use cartesian operation follow by map operation on RDD. But, this is very slow when the N is large. Is there a more cleaver way to do this for an arbitrary similarity function ? Cheers, Jao
Re: Build k-NN graph for large dataset
You could try dimensionality reduction (PCA or SVD) first. I would imagine that even if you could successfully compute similarities in the high-dimensional space you would probably run into the curse of dimensionality. On 26 Aug 2015, at 12:35, Jaonary Rabarisoa jaon...@gmail.com wrote: Dear all, I'm trying to find an efficient way to build a k-NN graph for a large dataset. Precisely, I have a large set of high dimensional vector (say d 1) and I want to build a graph where those high dimensional points are the vertices and each one is linked to the k-nearest neighbor based on some kind similarity defined on the vertex spaces. My problem is to implement an efficient algorithm to compute the weight matrix of the graph. I need to compute a N*N similarities and the only way I know is to use cartesian operation follow by map operation on RDD. But, this is very slow when the N is large. Is there a more cleaver way to do this for an arbitrary similarity function ? Cheers, Jao - To unsubscribe, e-mail: user-unsubscr...@spark.apache.org For additional commands, e-mail: user-h...@spark.apache.org
Re: Build k-NN graph for large dataset
If you don't want to compute all N^2 similarities, you need to implement some kind of blocking first. For example, LSH (locally sensitive hashing). A quick search gave this link to a Spark implementation: http://stackoverflow.com/questions/2771/spark-implementation-for-locality-sensitive-hashing On Wed, Aug 26, 2015 at 7:35 AM, Jaonary Rabarisoa jaon...@gmail.com wrote: Dear all, I'm trying to find an efficient way to build a k-NN graph for a large dataset. Precisely, I have a large set of high dimensional vector (say d 1) and I want to build a graph where those high dimensional points are the vertices and each one is linked to the k-nearest neighbor based on some kind similarity defined on the vertex spaces. My problem is to implement an efficient algorithm to compute the weight matrix of the graph. I need to compute a N*N similarities and the only way I know is to use cartesian operation follow by map operation on RDD. But, this is very slow when the N is large. Is there a more cleaver way to do this for an arbitrary similarity function ? Cheers, Jao
Re: Build k-NN graph for large dataset
Yes. And a paper that describes using grids (actually varying grids) is http://research.microsoft.com/en-us/um/people/jingdw/pubs%5CCVPR12-GraphConstruction.pdf In the Spark GraphX In Action book that Robin East and I are writing, we implement a drastically simplified version of this in chapter 7, which should become available in the MEAP mid-September. http://www.manning.com/books/spark-graphx-in-action From: Kristina Rogale Plazonic kpl...@gmail.com To: Jaonary Rabarisoa jaon...@gmail.com Cc: user user@spark.apache.org Sent: Wednesday, August 26, 2015 7:24 AM Subject: Re: Build k-NN graph for large dataset If you don't want to compute all N^2 similarities, you need to implement some kind of blocking first. For example, LSH (locally sensitive hashing). A quick search gave this link to a Spark implementation: http://stackoverflow.com/questions/2771/spark-implementation-for-locality-sensitive-hashing On Wed, Aug 26, 2015 at 7:35 AM, Jaonary Rabarisoa jaon...@gmail.com wrote: Dear all, I'm trying to find an efficient way to build a k-NN graph for a large dataset. Precisely, I have a large set of high dimensional vector (say d 1) and I want to build a graph where those high dimensional points are the vertices and each one is linked to the k-nearest neighbor based on some kind similarity defined on the vertex spaces. My problem is to implement an efficient algorithm to compute the weight matrix of the graph. I need to compute a N*N similarities and the only way I know is to use cartesian operation follow by map operation on RDD. But, this is very slow when the N is large. Is there a more cleaver way to do this for an arbitrary similarity function ? Cheers, Jao
Re: Build k-NN graph for large dataset
+1 to all of the above esp. Dimensionality reduction and locality sensitive hashing / min hashing. There's also an algorithm implemented in MLlib called DIMSUM which was developed at Twitter for this purpose. I've been meaning to try it and would be interested to hear about results you get. https://blog.twitter.com/2014/all-pairs-similarity-via-dimsum Charlie — Sent from Mailbox On Wednesday, Aug 26, 2015 at 09:57, Michael Malak michaelma...@yahoo.com.invalid, wrote: Yes. And a paper that describes using grids (actually varying grids) is http://research.microsoft.com/en-us/um/people/jingdw/pubs%5CCVPR12-GraphConstruction.pdf In the Spark GraphX In Action book that Robin East and I are writing, we implement a drastically simplified version of this in chapter 7, which should become available in the MEAP mid-September. http://www.manning.com/books/spark-graphx-in-action If you don't want to compute all N^2 similarities, you need to implement some kind of blocking first. For example, LSH (locally sensitive hashing). A quick search gave this link to a Spark implementation: http://stackoverflow.com/questions/2771/spark-implementation-for-locality-sensitive-hashing On Wed, Aug 26, 2015 at 7:35 AM, Jaonary Rabarisoa jaon...@gmail.com wrote: Dear all, I'm trying to find an efficient way to build a k-NN graph for a large dataset. Precisely, I have a large set of high dimensional vector (say d 1) and I want to build a graph where those high dimensional points are the vertices and each one is linked to the k-nearest neighbor based on some kind similarity defined on the vertex spaces. My problem is to implement an efficient algorithm to compute the weight matrix of the graph. I need to compute a N*N similarities and the only way I know is to use cartesian operation follow by map operation on RDD. But, this is very slow when the N is large. Is there a more cleaver way to do this for an arbitrary similarity function ? Cheers, Jao
