Patent ID: 7756342

Claim:
A method of optimizing a data classification for data representative of one or more physical parameters, comprising using a processor to perform the steps of: obtaining a first data set including a plurality of said data representative of said one or more physical parameters; dividing the data set into a plurality of data sets smaller than the first data set; calculating a manifold coordinate system for each of said data sets in said plurality of data sets by computing a shortest geodesic distance on a manifold between each neighboring pair of data points in said plurality of data; computing vantage point forests at a selected distance metric r; sorting N samples into neighborhoods by querying said vantage point forests; optionally expanding the neighborhood definition to ensure that at least K min neighbors in the neighborhood but no more than K max are assembled, and implementing said optional neighborhood definition with multiple vantage point forests at different values of r; connecting substantially all N samples to a landmark on a final geodesic distance graph to substantially eliminate isolated data points; estimating geodesic distances from the N samples to L landmarks to form an L×N landmarks geodesic distance matrix; extracting q eigenvalue-eigenvector pairs from a second order variation of an L×L sub-matrix of the L×N landmarks geodesic distance matrix; and for each point that is not a landmark point projecting a vector of geodesic distances from each landmark computed in said vantage point forest computation step onto the q eigenvectors to obtain q manifold coordinates, thereby obtaining an optimized data classification set of the data representative of the physical parameters.