ranking: Ranking

In kernlab: Kernel-Based Machine Learning Lab

Ranking

Description

A universal ranking algorithm which assigns importance/ranking to data points given a query.

Usage

## S4 method for signature 'matrix'
ranking(x, y,
        kernel ="rbfdot", kpar = list(sigma = 1),
        scale = FALSE, alpha = 0.99, iterations = 600,
        edgegraph = FALSE, convergence = FALSE ,...)

## S4 method for signature 'kernelMatrix'
ranking(x, y,
        alpha = 0.99, iterations = 600, convergence = FALSE,...)

## S4 method for signature 'list'
ranking(x, y,
        kernel = "stringdot", kpar = list(length = 4, lambda = 0.5),
        alpha = 0.99, iterations = 600, convergence = FALSE, ...)

Arguments

x	a matrix containing the data to be ranked, or the kernel matrix of data to be ranked or a list of character vectors
y	The index of the query point in the data matrix or a vector of length equal to the rows of the data matrix having a one at the index of the query points index and zero at all the other points.
kernel	the kernel function used in training and predicting. This parameter can be set to any function, of class kernel, which computes a dot product between two vector arguments. kernlab provides the most popular kernel functions which can be used by setting the kernel parameter to the following strings: rbfdot Radial Basis kernel function "Gaussian" polydot Polynomial kernel function vanilladot Linear kernel function tanhdot Hyperbolic tangent kernel function laplacedot Laplacian kernel function besseldot Bessel kernel function anovadot ANOVA RBF kernel function splinedot Spline kernel The kernel parameter can also be set to a user defined function of class kernel by passing the function name as an argument.
kpar	the list of hyper-parameters (kernel parameters). This is a list which contains the parameters to be used with the kernel function. For valid parameters for existing kernels are : sigma inverse kernel width for the Radial Basis kernel function "rbfdot" and the Laplacian kernel "laplacedot". degree, scale, offset for the Polynomial kernel "polydot" scale, offset for the Hyperbolic tangent kernel function "tanhdot" sigma, order, degree for the Bessel kernel "besseldot". sigma, degree for the ANOVA kernel "anovadot". Hyper-parameters for user defined kernels can be passed through the kpar parameter as well.
scale	If TRUE the data matrix columns are scaled to zero mean and unit variance.
alpha	The alpha parameter takes values between 0 and 1 and is used to control the authoritative scores received from the unlabeled points. For 0 no global structure is found the algorithm ranks the points similarly to the original distance metric.
iterations	Maximum number of iterations
edgegraph	Construct edgegraph (only supported with the RBF kernel)
convergence	Include convergence matrix in results
...	Additional arguments

Details

A simple universal ranking algorithm which exploits the intrinsic global geometric structure of the data. In many real world applications this should be superior to a local method in which the data are simply ranked by pairwise Euclidean distances. Firstly a weighted network is defined on the data and an authoritative score is assigned to each query. The query points act as source nodes that continually pump their authoritative scores to the remaining points via the weighted network and the remaining points further spread the scores they received to their neighbors. This spreading process is repeated until convergence and the points are ranked according to their score at the end of the iterations.

Value

An S4 object of class ranking which extends the matrix class. The first column of the returned matrix contains the original index of the points in the data matrix the second column contains the final score received by each point and the third column the ranking of the point. The object contains the following slots :

edgegraph	Containing the edgegraph of the data points.
convergence	Containing the convergence matrix

Author(s)

Alexandros Karatzoglou
[e-mail]

References

D. Zhou, J. Weston, A. Gretton, O. Bousquet, B. Schoelkopf
Ranking on Data Manifolds
Advances in Neural Information Processing Systems 16.
MIT Press Cambridge Mass. 2004
https://papers.neurips.cc/paper/2447-ranking-on-data-manifolds.pdf

See Also

ranking-class, specc

Examples

data(spirals)

## create data from spirals
ran <- spirals[rowSums(abs(spirals) < 0.55) == 2,]

## rank points according to similarity to the most upper left point  
ranked <- ranking(ran, 54, kernel = "rbfdot",
                  kpar = list(sigma = 100), edgegraph = TRUE)
ranked[54, 2] <- max(ranked[-54, 2])
c<-1:86
op <- par(mfrow = c(1, 2),pty="s")
plot(ran)
plot(ran, cex=c[ranked[,3]]/40)

kernlab documentation built on Sept. 12, 2024, 6:51 a.m.