Distributable event prediction and machine learning recognition system

Abstract

A computing device predicts occurrence of an event or classifies an object using distributed unlabeled data. A Laplacian matrix is computed using a kernel function. A predefined number of eigenvectors is selected from a decomposed Laplacian matrix to define a decomposition matrix. A gradient value is computed as a function of the defined decomposition matrix, a plurality of sparse coefficients, and a label matrix, a value of each coefficient of the plurality of sparse coefficients is updated based on the computed gradient value, and the computations are repeated until a convergence parameter value indicates the plurality of sparse coefficients have converged. A classification matrix is defined using the plurality of sparse coefficients to determine the target variable value for each observation vector of the plurality of unclassified observation vectors. The target variable value for each observation vector of the plurality of unclassified observation vectors is output.