Computing sparse pairwise distance matrix in R

I have a NxM matrix and I want to compute the NxN matrix of Euclidean distances between the M points. In my problem, N is about 100,000. As I plan to use this matrix for a k-nearest neighbor algorithm, I only need to keep the k smallest distances, so the resulting NxN matrix is very sparse. This is in contrast to what comes out of dist(), for example, which would r开发者_StackOverflow社区esult in a dense matrix (and probably storage problems for my size N).

The packages for kNN that I've found so far (knnflex, kknn, etc) all appear to use dense matrices. Also, the Matrix package does not offer a pairwise distance function.

Closer to my goal, I see that the spam package has a nearest.dist() function that allows one to only consider distances less than some threshold, delta. In my case, however, a particular value of delta may produce too many distances (so that I have to store the NxN matrix densely) or too few distances (so that I can't use kNN).

I have seen previous discussion on trying to perform k-means clustering using the bigmemory/biganalytics packages, but it doesn't seem like I can leverage these methods in this case.

Does anybody know a function/implementation that will compute a distance matrix in a sparse fashion in R? My (dreaded) backup plan is to have two for loops and save results in a Matrix object.

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Well, we can't have you resorting to for-loops, now can we :)

There is of course the question of how to represent the sparse matrix. A simple way is to have it only contain the indices of the points that are closest (and recalculate as needed). But in the solution below, I put both distance ('d1' etc) and index ('i1' etc) in a single matrix:

sparseDist <- function(m, k) {
    m <- t(m)
    n <- ncol(m)
    d <- vapply( seq_len(n-1L), function(i) { 
        d<-colSums((m[, seq(i+1L, n), drop=FALSE]-m[,i])^2)
        o<-sort.list(d, na.last=NA, method='quick')[seq_len(k)]
        c(sqrt(d[o]), o+i) 
        }, numeric(2*k)
    )
    dimnames(d) <- list(c(paste('d', seq_len(k), sep=''),
        paste('i', seq_len(k), sep='')), colnames(m)[-n])
    d
}

Trying it out on 9 2d-points:

> m <- matrix(c(0,0, 1.1,0, 2,0, 0,1.2, 1.1,1.2, 2,1.2, 0,2, 1.1,2, 2,2),
              9, byrow=TRUE, dimnames=list(letters[1:9], letters[24:25]))
> print(dist(m), digits=2)
    a   b   c   d   e   f   g   h
b 1.1                            
c 2.0 0.9                        
d 1.2 1.6 2.3                    
e 1.6 1.2 1.5 1.1                
f 2.3 1.5 1.2 2.0 0.9            
g 2.0 2.3 2.8 0.8 1.4 2.2        
h 2.3 2.0 2.2 1.4 0.8 1.2 1.1    
i 2.8 2.2 2.0 2.2 1.2 0.8 2.0 0.9
> print(sparseDist(m, 3), digits=2)
     a   b   c   d   e   f   g   h
d1 1.1 0.9 1.2 0.8 0.8 0.8 1.1 0.9
d2 1.2 1.2 1.5 1.1 0.9 1.2 2.0  NA
d3 1.6 1.5 2.0 1.4 1.2 2.2  NA  NA
i1 2.0 3.0 6.0 7.0 8.0 9.0 8.0 9.0
i2 4.0 5.0 5.0 5.0 6.0 8.0 9.0  NA
i3 5.0 6.0 9.0 8.0 9.0 7.0  NA  NA

And trying it on a larger problem (10k points). Still, on 100k points and more dimensions it will take a long time (like 15-30 minutes).

n<-1e4; m<-3; m=matrix(runif(n*m), n)
system.time( d <- sparseDist(m, 3) ) # 9 seconds on my machine...

P.S. Just noted that you posted an answer as I was writing this: the solution here is roughly twice as fast because it doesn't calculate the same distance twice (the distance between points 1 and 13 is the same as between points 13 and 1).

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For now I am using the following, inspired by this answer. The output is a n x k matrix where element (i,k) is the index of the data point that is the kth closest to i.

n <- 10
d <- 3
x <- matrix(rnorm(n * d), ncol = n)

min.k.dists <- function(x,k=5) {
  apply(x,2,function(r) {
    b <- colSums((x - r)^2)
    o <- order(b)
    o[1:k]
  })
}

min.k.dists(x)  # first row should be 1:ncol(x); these points have distance 0
dist(t(x))      # can check answer against this

If one is worried about how ties are handled and whatnot, perhaps rank() should be incorporated.

The above code seems somewhat fast, but I'm sure it could be improved (though I don't have time to go the C or fortran route). So I'm still open to fast and sparse implementations of the above.

Below I include a parallelized version that I ended up using:

min.k.dists <- function(x,k=5,cores=1) {
  require(multicore)
  xx <- as.list(as.data.frame(x))
  names(xx) <- c()
  m <- mclapply(xx,function(r) {
    b <- colSums((x - r)^2)
    o <- order(b)
    o[1:k]
  },mc.cores=cores)
  t(do.call(rbind,m))
}

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If you want to keep the logic of your min.k.dist function and return duplicate distances, you might want to consider modifying it a bit. It seems pointless to return the first line with 0 distance, right? ...and by incorporating some of the tricks in my other answer, you can speed up your version by some 30%:

min.k.dists2 <- function(x, k=4L) {
  k <- max(2L, k + 1L)
  apply(x, 2, function(r) {
    sort.list(colSums((x - r)^2), na.last=NA, method='quick')[2:k]
  })
}

> n<-1e4; m<-3; m=matrix(runif(n*m), n)
> system.time(d <- min.k.dists(t(m), 4)) #To get 3 nearest neighbours and itself
   user  system elapsed 
  17.26    0.00   17.30 
> system.time(d <- min.k.dists2(t(m), 3)) #To get 3 nearest neighbours
   user  system elapsed 
   12.7     0.0    12.7