algorithm - Algorithmically correcting noisy distance measurements between data points on a number line -


i have application have data points x can mapped number line. however, don't know value on number line is. fortunately, don't need know exactly values are, need know distances between points (i.e. 0-point on number line arbitrary, , can reflected without affecting anything).

i have input set of transitive distance measurements d[i,j] between points. additionally, distance measurements oriented (i.e. d[i,j] = -d[j,i], d[i,j] > 0 indicating x[i] further right on number line x[j]).

first challenge: don't have of values of d, have control on values obtain.

ideally, wouldn't big of problem because choose pairs [i,j] d forms spanning tree (interpreting adjacency matrix of graph) , distance between x[i] , x[j] path length of walk between nodes i , j on tree. unfortunately...

second challenge: distance measurements noisy. in cases, noise small. however, in rare cases distance measurements wildly inaccurate.

so. problem. use multiple distance measurements between data points denoise these noisy distance measurements. here's wish list, of may turn out infeasible:

  • an algorithm maps points in x number line (with arbitrary 0-point) there single, unambiguous, transitive distance between x[i] , x[j], , clear 1 right of other.
  • the algorithm works graph d sparse, not tree. perhaps has property there @ least 2 paths between 2 nodes.
  • because multiple paths through d between 2 points represent different prospective distance measurements, use loss function minimizes difference of inter-data point distances in projection integrated value of distances implied d.
  • the algorithm uses loss function saturates high values. take care of case distances inaccurate, i'm worried make problem non-convex. l1 penalty might enough.

my first thought multidimensional scaling literature since it's concerned distance-preserving embeddings, don't think problem posed correctly. mds seems assume have single, unambiguous distance measurement between each point. that's not set up.

i'm interested in literature or algorithms people can point me to, whether aspect of problem, or similar problem, or same problem. thanks!

if can put l1 norm, think can minimize linear programming problem - tells convex problem, variety of other techniques, such simple hill-climbing , iteratively reweighted least squares, should converge global minimum.

arbitrarily set x0 = 0.

for each pair xi, xj have measurements add xi - xj - dij <= tij, dij - xi + xj <= tij, dij measurement

minimise sum_ij tij - minimised @ tij = |xi - xj - dij|, minimizes sum of l1 norms of errors.


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