SPIDER The Spider Objects

Manifold Ranking

   a = mrank(hyperParam) 
   Generates an mrank object with given hyperparameters.
     Hyperparameters (with defaults)
     child=kernel('rbf')  -- the kernel is stored as a member called "child"
     alpha=0.99           -- parameter governing convergence rate
     iterations=600       -- number of iterations of algorithm
     use_edge_graph=0     -- if turned on, sparsify kernel using edge graph
     normalize=0          -- normalize in the input space? 1 yes, 0 no
     standardize=[]       -- centre and standardize data? 1 yes, 0 no
                             default behaviour is to do so, except for
                             linear and polynomial-based kernels
                             (good for maintaining sparsity).
      There is no real need to split the algorithm into TRAIN and TEST
      phases - this is done for consistency with other algorithm classes.
      If labels are supplied in DAT.y, a single call to TRAIN is sufficient.
      If DAT.y is empty, TRAIN merely stores the data in the algorithm
      object, waiting for query points to be supplied by TEST.
      [DAT, A] = TRAIN(A, DAT);
      Points with Y=0 within DAT are considered unlabelled, and points with
      non-zero Y are the query points. The unlabelled points are ranked
      according to their similarity to the query points, computed via an
      iterative spreading activation network algorithm. A.result on exit
      contains [I S R] where I is the original index, S the ranking score
      and R the rank of each point. Results can be plotted with PLOT(A).
      DAT is unchanged.
      DAT = TEST(A, DAT)
      If DAT contains labels, throw away data stored in A and procede using
      DAT as for TRAIN. If not, use DAT as the query points, and use any
      unlabelled points from the data stored in A as the unlabelled points
      (stored labelled points, i.e. those that were used as query points in
      the previous run, are removed). On exit, the y field of DAT contains
      ranking scores. The results can be plotted with PLOTRANKING(DAT).
      PLOT(A) -- see HELP MRANK/PLOT
   d = gen(spiral({'m=100','n=0.5','noise=0.35'}));
   labelled = [50];
   d.Y = double(ismember([1:length(d.Y)]', labelled));
   a = mrank;
   a.child = kernel('rbf',0.1);
   a.use_edge_graph = 1;
   [dd aa] = train(a, d);
   plot(aa, 'etrc')
          (plots [e]dge graph, minimal spanning [t]ree, [r]anking and [c]onvergence)

Reference : Ranking on Data Manifolds
Author : Dengyong Zhou , Jason weston , Arthur Gretton , OlivierBousquet and Bernhard Schölkopf
Link : http://www.kyb.tuebingen.mpg.de/publications/pdfs/pdf2290.pdf