Abstract: During a feature-selection technique, an electronic device calculates combinations of features and noise vectors, where a given combination corresponds to a given feature and a given noise vector. Then, the electronic device determines statistical associations between information specifying types of events and the combinations, where a given statistical association corresponds to the types of events and a given combination. Moreover, the electronic device identifies a noise threshold associated with the combinations. Next, for a group of combinations having statistical associations equal to or greater than the noise threshold, the electronic device selects a subset of the features based at least in part on a first 10 aggregate property of the group of combinations, where the first aggregate property comprises numbers of occurrences of the features in the group of combinations.

Abstract: During a feature-selection technique, an electronic device calculates combinations of features and noise vectors, where a given combination corresponds to a given feature and a given noise vector. Then, the electronic device determines statistical associations between information specifying types of events and the combinations, where a given statistical association corresponds to the types of events and a given combination. Moreover, the electronic device identifies a noise threshold associated with the combinations. Next, for a group of combinations having statistical associations equal to or greater than the noise threshold, the electronic device selects a subset of the features based at least in part on a first aggregate property of the group of combinations, where the first aggregate property comprises numbers of occurrences of the features in the group of combinations.

Abstract: An electronic device may access one or more records associated with an individual based at least in part on an identifier of the individual. Then, based at least in part on values of features associated with a predetermined segment in a population that includes the individual and values of the features in the one or more records, the electronic device assigns the individual to the predetermined segment. Note that the features may be identified based at least in part on count statistics of the features in combinations of features for the population, and the predetermined segment may be based at least in part on distances (such as Euclidean distances) computed using values of the features. Next, the electronic device provides a recommendation for the individual, where the recommendation is based at least in part on one or more attributes associated with one or more other individuals in the predetermined segment.

Abstract: During a feature-selection technique, an electronic device calculates combinations of features and noise vectors, where a given combination corresponds to a given feature and a given noise vector. Then, the electronic device determines statistical associations between information specifying types of events and the combinations, where a given statistical association corresponds to the types of events and a given combination. Moreover, the electronic device identifies a noise threshold associated with the combinations. Next, for a group of combinations having statistical associations equal to or greater than the noise threshold, the electronic device selects a subset of the features based at least in part on a first aggregate property of the group of combinations, where the first aggregate property comprises numbers of occurrences of the features in the group of combinations.