Fast training of support vector data description using sampling

A computing device comprising:

• a processor;
• and
• a non-transitory computer-readable medium operably coupled to the processor, the computer-readable medium having computer-readable instructions stored thereon that, when executed by the processor, cause the computing device to
• randomly select a first set of observation vectors from a training dataset, wherein a number of the first set of observation vectors is a predefined sample size;
• compute a first optimal value of an objective function defined for a support vector data description (SVDD) model using the selected first set of observation vectors to define a first set of support vectors, wherein the first set of support vectors define a first data description for the training dataset;
• (a) randomly select a second set of observation vectors from the training dataset, wherein a number of the second set of observation vectors is the predefined sample size;
• (b) compute a second optimal value of the objective function using the selected second set of observation vectors to define a second set of support vectors, wherein the second set of support vectors define a second data description for the training dataset;
• (c) update the first set of support vectors to include the defined second set of support vectors;
• (d) compute a third optimal value of the objective function using the updated first set of support vectors to define a third set of support vectors, wherein the third set of support vectors define a third data description for the training dataset;
• (e) compute a value of a stop parameter;
• (f) determine whether or not a stop condition is satisfied by comparing the computed value to a stop criterion;
• (g) when the stop condition is not satisfied,
• define the first set of support vectors as the defined third set of support vectors;
• and
• repeat (a)-(g) until the stop condition is satisfied;
• and
• when the stop condition is satisfied, output the defined third set of support vectors for identifying an outlier in a scoring dataset.

A method of determining a support vector data description for outlier identification, the method comprising:

• randomly selecting, by a computing device, a first set of observation vectors from a training dataset, wherein a number of the first set of observation vectors is a predefined sample size;
• computing, by the computing device, a first optimal value of an objective function defined for a support vector data description (SVDD) model using the selected first set of observation vectors to define a first set of support vectors, wherein the first set of support vectors define a first data description for the training dataset;
• (a) randomly selecting, by the computing device, a second set of observation vectors from the training dataset, wherein a number of the second set of observation vectors is the predefined sample size;
• (b) computing, by the computing device, a second optimal value of the objective function using the selected second set of observation vectors to define a second set of support vectors, wherein the second set of support vectors define a second data description for the training dataset;
• (c) updating, by the computing device, the first set of support vectors to include the defined second set of support vectors;
• (d) computing, by the computing device, a third optimal value of the objective function using the updated first set of support vectors to define a third set of support vectors, wherein the third set of support vectors define a third data description for the training dataset;
• (e) computing, by the computing device, a value of a stop parameter;
• (f) determining, by the computing device, whether or not a stop condition is satisfied by comparing the computed value to a stop criterion;
• (g) when the stop condition is not satisfied,
• defining, by the computing device, the first set of support vectors as the defined third set of support vectors;
• and
• repeating (a)-(g), by the computing device, until the stop condition is satisfied;
• and
• when the stop condition is satisfied, outputting, by the computing device, the defined third set of support vectors for identifying an outlier in a scoring dataset.