Lets you generate an N-dimensional sparse array from a table of explicit entries. S=ndSparse.build(Coordinates,Values,nzmax) The class also has a variety of static methods that can be used to construct instances of the class. S=ndSparse(X,) is equivalent to reshape(ndSparse(X),). S can be reshaped into an N-dimensional sparse array using its RESHAPE method, for arbitrary N. S=ndSparse(X) where X is an ordinary MATLAB sparse matrix converts X into an ndSparse object. I find the class useful mainly for moderate dimensional things like edge detection in 3D imaging, where you often want to hold a sparse 3D edge map. This leads to certain memory strains when using large numbers of dimensions. In fact, if the intended array has dimensions MxNxP.YxZ, then the class will store it internally as an ordinary 2D sparse array of dimensions (M*N*P*.*Y)xZ. In other words, the sparse data must first be able to exist as an ordinary 2D MATLAB sparse matrix before being made N-dimensional. However, it should really be thought of as a way of starting with an ordinary MATLAB sparse matrix and reshaping it to have N dimensions.

This submission defines a class of N-dimensional sparse arrays for N possibly greater than 2.

Mathworks Matlab R2010b Iso Full