Systems and Methods for Programmatic Labeling of Training Data for Machine Learning Models via Clustering
OwnerFait Poms, Naveen Iyer +2inventors
InventorsFait Poms, Naveen Iyer, Braden Hancock, Roshni Malani
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Abstract
Embodiments are directed to an approach to semi-automatically (programmatically) generate labels for data based on implementation of a clustering technique and can be used to implement a form of programmatic labeling to accelerate the development of classifiers and other forms of models. The disclosed methodology is particularly helpful in generating labels or annotations for unstructured data. In some embodiments, the disclosed approach may be used with data in the form of text, images, or other form of unstructured data.
Claims20
A method of training a machine learning model, comprising:
- generating a real-valued representation for each datapoint in a dataset;
- based on a similarity between the generated representations, forming one or more groups or clusters of datapoints;
- representing each formed group or cluster by a unique identifier;
- for each group or cluster, training a classifier to classify a datapoint as either inside or outside the group or cluster;
- storing each trained classifier and associating the stored trained classifier with the cluster or group's unique identifier;
- for each new datapoint, using the new datapoint as input to each trained classifier and determining a most likely cluster or group to which the new datapoint is assigned;
- assigning a label to the new datapoint based on the identifier of the cluster or group to which the new datapoint is assigned;
- and
- using a plurality of new datapoints and the new datapoints' assigned labels to train a machine learning model.
A system, comprising:
- one or more electronic processors configured to execute a set of computer-executable instructions;
- and
- one or more non-transitory electronic data storage media containing the set of computer-executable instructions, wherein when executed, the instructions cause the one or more electronic processors to
- generate a real-valued representation for each datapoint in a dataset;
- based on a similarity between the generated representations, form one or more groups or clusters of datapoints;
- represent each formed group or cluster by a unique identifier;
- for each group or cluster, train a classifier to classify a datapoint as either inside or outside the group or cluster;
- store each trained classifier and associate the stored trained classifier with the cluster or group's unique identifier;
- for each new datapoint, use the new datapoint as input to each trained classifier and determine a most likely cluster or group to which the new datapoint is assigned;
- assign a label to the new datapoint based on the identifier of the cluster or group to which the new datapoint is assigned;
- and
- use a plurality of new datapoints and the new datapoints' assigned labels to train a machine learning model.
One or more non-transitory computer-readable media comprising a set of computer-executable instructions that when executed by one or more programmed electronic processors, cause the processors to:
- generate a real-valued representation for each datapoint in a dataset;
- based on a similarity between the generated representations, form one or more groups or clusters of datapoints;
- represent each formed group or cluster by a unique identifier;
- for each group or cluster, train a classifier to classify a datapoint as either inside or outside the group or cluster;
- store each trained classifier and associate the stored trained classifier with the cluster or group's unique identifier;
- for each new datapoint, use the new datapoint as input to each trained classifier and determine a most likely cluster or group to which the new datapoint is assigned;
- assign a label to the new datapoint based on the identifier of the cluster or group to which the new datapoint is assigned;
- and
- use a plurality of new datapoints and the new datapoints' assigned labels to train a machine learning model.
The one or more non-transitory computer-readable media of clause 15, wherein instead of generating a real-valued representation for each datapoint in a dataset, a plurality of real-valued representations for each datapoint in a dataset are generated, and for each of the plurality of representations, the method proceeds as described.
Dependencies are inferred from the claim text.