dca: Discriminative Component Analysis In dml: Distance Metric Learning in R

Description

Performs discriminative component analysis on the given data.

Usage

 1 dca(data, chunks, neglinks, useD = NULL)

Arguments

 data n * d data matrix. n is the number of data points, d is the dimension of the data. Each data point is a row in the matrix. chunks length n vector describing the chunklets: -1 in the i th place means point i doesn't belong to any chunklet; integer j in place i means point i belongs to chunklet j. The chunklets indexes should be 1:(number of chunklets). neglinks s * s symmetric matrix describing the negative relationship between all the s chunklets. For the element neglinks_{ij}: neglinks_{ij} = 1 means chunklet i and chunklet j have negative constraint(s); neglinks_{ij} = 0 means chunklet i and chunklet j don't have negative constraints or we don't have information about that. useD Integer. Optional. When not given, DCA is done in the original dimension and B is full rank. When useD is given, DCA is preceded by constraints based LDA which reduces the dimension to useD. B in this case is of rank useD.

Details

Put DCA function details here.

Value

list of the DCA results:

 B DCA suggested Mahalanobis matrix DCA DCA suggested transformation of the data. The dimension is (original data dimension) * (useD) newData DCA transformed data

For every two original data points (x1, x2) in newData (y1, y2):

(x2 - x1)' * B * (x2 - x1) = || (x2 - x1) * A ||^2 = || y2 - y1 ||^2

Note

Put some note here.