PARSIMONIOUS MULTI-RESOLUTION VALUE-ITEM LISTS

Systems and methods are provided for parsimonious representation of large sets of multi-resolution value-item lists. A hierarchical data structure associated with the lists and conditioning variables is learnt while exploiting both semantics encoded in target variables and a notion of nearness among nodes at the same detail level in the hierarchical data structure. Such a level of description can be dictated by a depth in a tree data structure. A compression scheme that relies on (i) a similarity metric and (ii) recursive greedy pairing of value-item lists in order to promote elements populating a specific tree node upwards in the tree facilitates a parsimonious representation of the compressed hierarchic structure.