Method, apparatus, and system for quantifying a diversity in a machine learning training data set
AssigneeHERE Global B.V.
InventorsRichard KWANT, Anish MITTAL, David LAWLOR
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Abstract
An approach is provided for quantifying a diversity of a machine learning training data set. The approach involves creating a matrix data structure storing a plurality of feature data records describing the observations in the training data set. The approach also involves computing a covariance of the matrix data structure. For example, in one embodiment, the covariance is based on a stable rank of a covariance matrix. The approach further involves determining the diversity value of the observations based on the computed covariance.
Claims20
A computer-implemented method for quantifying a diversity value of observations in a training data set for training a machine learning model comprising:
- creating, by a processor, a matrix data structure storing a plurality of feature data records describing the observations in the training data set;
- computing a covariance of the matrix data structure;
- and
- determining the diversity value of the training data set based on the computed covariance.
An apparatus for quantifying a diversity value of observations in a training data set for training a machine learning model comprising:
- at least one processor;
- and
- at least one memory including computer program code for one or more programs,
- the at least one memory and the computer program code configured to, with the at least one processor, cause the apparatus to perform at least the following,
- create a matrix storing a plurality of features describing the observations in the training data set;
- computing a covariance of the matrix;
- and
- determining the diversity value of the training data set based on the computed covariance.
A non-transitory computer-readable storage medium for quantifying a diversity value of observations in a training data set for training a machine learning model, carrying one or more sequences of one or more instructions which, when executed by one or more processors, cause an apparatus to perform:
- creating a matrix data structure storing a plurality of feature data records describing the observations in the training data set;
- computing a covariance of the matrix data structure;
- and
- determining the diversity value of the training data set based on the computed covariance.
Dependencies are inferred from the claim text.