Systems and methods for robust pattern classification

摘要

Certain embodiments relate to systems and methods for performing data discrimination using dimensionality reduction techniques. Particularly the Sparse Matrix Transform (SMT) is applied to more efficiently determine the Fischer Discrimination vector for a given dataset. Adjustments to the SMT facilitate more robust identification of the Fischer Discrimination vector in view of various resource constraints.

主权项

1. A method, implemented on an electronic device, for generating physical sensor data classifiers, the method comprising:
receiving a plurality of physical sensor data; identifying a projection vector, comprising:
using a search algorithm comprising a metric function to identify correlated data represented by a sub-matrix of the plurality of sensor data, andcalculating one or more eigenvalues associated with the metric function at least in part by transforming the plurality of physical sensor data using a sparse matrix transform (SMT),wherein transforming using the SMT comprises reducing off-diagonal elements of the sub-matrix, wherein the off-diagonal elements represent a level of correlation of the correlated data; and producing physical sensor data classifiers by projecting at least a portion of the physical sensor data upon the projection vector.