Patent ID: 11921820
Assignee: FORTINET, INC.
Field: Computer technology (Electrical engineering)
Classification: CPC G | IPC G

Claim 0:
1. A method comprising:
receiving, by a processing resource of a computing system, a first set of feature vectors, wherein the first set of feature vectors are un-labeled;
grouping, by the processing resource, the first set of feature vectors into a plurality of clusters within a vector space having fewer dimensions than the first set of feature vectors by applying a homomorphic dimensionality reduction algorithm to the first set of feature vectors and performing centroid-based clustering;
identifying, by the processing resource, an optimal set of clusters among the plurality of clusters by performing a convex optimization process on the plurality of clusters;
minimizing, by the processing resource, vector labeling by selecting a plurality of ground truth representative vectors including a representative vector from each cluster of the optimal set of clusters;
creating, by the processing resource, a set of labeled feature vectors based on labels received from an oracle for each of the plurality of representative vectors;
training, by the processing resource, a machine-learning model for multiclass classification based on the set of labeled feature vectors; and
training the machine-learning model with inductive learning, wherein the inductive learning comprises:
selecting an unlabeled feature vector from the first set of feature vectors;
classifying the un-labeled feature vector using the machine learning model to get a model classified cluster with a confidence score;
determining whether the confidence score is greater than a threshold; and

when said determining is affirmative:
determining a Mahalanobis distance of the un-labeled feature vector with respect to each labeled feature vector of the first set of feature vectors;
determining a statistically matching cluster of labeled feature vectors to which the un-labeled feature vector is closest based on the determined Mahalanobis distance;
determining whether the model classified cluster and the statistically matching cluster are the same; and
when the model classified cluster and the statistically matching cluster are determined to be the same:
labeling the un-labeled feature vector based on the label associated with the model classified cluster; and
model fitting the machine learning model based on the labeling.