Patent ID: 7299213

Claim:
A method for extracting information from a dataset comprising identifying significant features within the dataset containing data items, at least a portion of which comprise irrelevant detail, wherein at least a portion of the data items in the dataset have associated labels, comprising: downloading the dataset into a memory of a computer having a processor for executing a plurality of clustering kernels; filling a pairwise similarity matrix K in the memory for all pairs of data items i and j in the dataset; applying the plurality of clustering kernels to the dataset in the memory to define distinct clusters of datapoints in feature space; determining an alignment between the clusters in feature space according to the relationship A ^ = ∑ i , j ⁢ y i ⁢ y j ⁢ K ⁡ ( i , j ) , where y i y j is a labels vector; selecting for execution by the processor the clustering kernel in the memory that produces a maximally aligned solution that is consistent with the labels; and separating the significant features from the irrelevant detail in the memory by processing the data items in the dataset using the selected kernel; generating an output in the memory comprising the significant features identified by the selected kernel; and transferring the output from the memory to a media.