Patent ID: 11928159
Assignee: GOOGLE LLC
Field: Computer technology (Electrical engineering)
Classification: CPC G | IPC G

Claim 0:
1. A computer-implemented method for generating a machine-learned model based on performing clustering with improved privacy and computational efficiency, the method comprising:
obtaining, by a computing system comprising one or more computing devices, data descriptive of a plurality of input datapoints expressed in a first dimensional space;
projecting, by the computing system, the plurality of input datapoints into a second dimensional space that has a fewer number of dimensions than the first dimensional space;
performing, by the computing system, a clustering algorithm to identify a plurality of clusters within the second dimensional space and for the input datapoints, wherein performing the clustering algorithm comprises, for each of one or more iterations:
defining, by the computing system, a plurality of subsets of neighboring datapoints respectively for the plurality of input datapoints, wherein the respective subset of neighboring datapoints for each input datapoint includes all input datapoints in a cover within a threshold distance of the input datapoint;
performing, by the computing system, a sparse selection technique on the plurality of subsets of neighboring datapoints to select one of the plurality of clusters; and

determining, by the computing system, a respective cluster center within the first dimensional space for each of the plurality of clusters based on a DensestBall algorithm and the plurality of clusters within the second dimensional space; and
training, by the computing system, the machine-learned model by updating one or more parameters of the machine-learned model, wherein updating the one or more parameters is based on employing the respective cluster center for each of the plurality of clusters to evaluate a loss function of the machine-learned model.