Hierarchical sparse dictionary learning (HiSDL) for heterogeneous high-dimensional time series

A system for hierarchical sparse dictionary learning (“HiSDL”) to construct a learned dictionary regularized by an a priori over-complete dictionary, the system comprising:

• an a priori over-complete dictionary generator configured to provide at least one a priori over-complete dictionary for regularization;
• a sparse coder configured to perform sparse coding of the at least one a priori over-complete dictionary to provide a sparse coded dictionary;
• using a processor, a dictionary updater configured to update the sparse coded dictionary with regularization using at least one auxiliary variable to provide a learned dictionary;
• a convergence determination device configured to determine convergence of the learned dictionary to an input data set;
• and
• an output device configured to output the learned dictionary regularized by the at least one a priori over-complete dictionary when the learned dictionary converges to the input data set.

A computer program product is provided that includes a non-transitory computer readable storage medium having computer readable program code embodied therein for a method for hierarchical sparse dictionary learning (“HiSDL”) to construct a learned dictionary regularized by an a priori over-complete dictionary, the method comprising:

• providing at least one a priori over-complete dictionary for regularization;
• performing sparse coding of the at least one a priori over-complete dictionary to provide a sparse coded dictionary;
• using a processor, updating the sparse coded dictionary with regularization using at least one auxiliary variables to provide a learned dictionary;
• determining whether the learned dictionary converges to an input data set;
• and
• outputting the learned dictionary regularized by the at least one a priori over-complete dictionary when the learned dictionary converges to the input data set.