Patent ID: 11972329
Assignee: XEROX CORPORATION
Field: Computer technology (Electrical engineering)
Classification: CPC G | IPC G

Claim 14:
15. A non-transitory computer-readable storage medium storing instructions which when executed by a computer system cause the computer system to perform a method for facilitating multi-label classification for a machine learning mechanism, the method comprising:
storing a set of training vectors, wherein a respective vector represents an object, wherein a respective vector is associated with one or more feature labels that belong to a label set, and wherein a respective feature label corresponds to a feature of the object;
receiving an input vector representing a human-recognizable input object, wherein the input vector is associated with a set of input labels, and wherein a respective input label corresponds to an input feature of the input object; and
classifying the input object using the machine learning mechanism, independently of supervision, on the computer system;
wherein the machine learning mechanism performs the classification based on the set of input labels by:
sampling the set of training vectors to select a subset of training vectors representative of the set of training vectors for the set of input labels, thereby reducing computational complexity for subsequent operations;
determining a similarity value between a respective input label and the subset of training vectors to generate a set of similarity values for the input label, thereby generating a plurality of sets of similarity values corresponding to the set of input labels, wherein the set of similarity values indicates a likelihood of an input feature indicated by the input label being associated with the input object;
normalizing and aggregating a respective set of similarity values of the plurality of sets of similarity values associated with a respective input label;
learning one or more input labels from the set of input labels for the input object based on the corresponding sets of normalized and aggregated similarity values; and
recursively learning one or more sub-labels associated with a respective label of the one or more input labels, wherein learning a respective sub-label comprises determining a set of similarity values for the sub-label with respect to the subset of training vectors.