A computing device determines an SVDD to identify an outlier in a dataset. First and second sets of observation vectors of a predefined sample size are randomly selected from a training dataset. First and second optimal values are computed using the first and second observation vectors to define a first set of support vectors and a second set of support vectors. A third optimal value is computed using the first set of support vectors updated to include the second set of support vectors to define a third set of support vectors. Whether or not a stop condition is satisfied is determined by comparing a computed value to a stop criterion. When the stop condition is not satisfied, the first set of support vectors is defined as the third set of support vectors, and operations are repeated until the stop condition is satisfied. The third set of support vectors is output.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A non-transitory computer-readable medium having stored thereon computer-readable instructions that when executed by a computing device 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. 2. The non-transitory computer-readable medium of claim 1, wherein the objective function defined for the SVDD model is max(Σi=1n αiK(xi,xi)−Σi=1n Σj=1n αiαjK(xi,xj)), subject to Σi=1n αi=1 and 0≦αi≦C, ∇i=1, . . . , n, where K(xi,xj) is a kernel function, n is the predefined sample size, C=1/nf where f is an expected outlier fraction, xi are the selected observation vectors for each computation, and αi are Lagrange constants. 3. The non-transitory computer-readable medium of claim 2, wherein the expected outlier fraction is a predefined input value. 4. The non-transitory computer-readable medium of claim 2, wherein the xi that have 0<αi≦C are the defined set of support vectors for each computation. 5. The non-transitory computer-readable medium of claim 4, wherein, when the stop condition is satisfied, the computer-readable instructions further cause the computing device to output the Lagrange constants αk for each of the defined third set of support vectors for identifying the outlier. 6. The non-transitory computer-readable medium of claim 2, wherein the kernel function is a Gaussian kernel function. 7. The non-transitory computer-readable medium of claim 4, wherein, when the stop condition is satisfied, the computer-readable instructions further cause the computing device to compute a threshold using the defined third set of support vectors. 8. The non-transitory computer-readable medium of claim 7, wherein the threshold is computed using R2=K(xk,xk)−2Σi=1NαiK(xi,xk)+Σi=1NΣj=1N αiαjK(xi, xj), where xk is any support vector of the set of support vectors for each computation that have 0<αi<C, xi and xj are the defined support vectors for each computation, αi and αj are the Lagrange constants of the associated support vector, and N is a number of support vectors included in the defined set of support vectors for each computation. 9. The non-transitory computer-readable medium of claim 8, wherein, when the stop condition is satisfied, the computer-readable instructions further cause the computing device to output the computed threshold for identifying the outlier. 10. The non-transitory computer-readable medium of claim 9, wherein, after outputting the defined third set of support vectors, the computer-readable instructions further cause the computing device to: read an observation vector from a scoring dataset; compute a distance value using the defined third set of support vectors, the Lagrange constants, and the read observation vector; and when the computed distance value is greater than the computed threshold, identify the read observation vector as an outlier. 11. The non-transitory computer-readable medium of claim 10, wherein the distance value is computed using dist2 (z)=K(z,z)−2Σi=1NαiK(xi,z)+Σi=1N Σj=1N αiαjK(xi, xj), where z is the read observation vector. 12. The non-transitory computer-readable medium of claim 10, wherein when the computed distance value is not greater than the computed threshold, the read observation vector is not identified as an outlier. 13. The non-transitory computer-readable medium of claim 1, wherein each observation vector includes a plurality of values, wherein each value of the plurality of values is associated with a variable to define a plurality of variables, wherein each variable of the plurality of variables describes a characteristic of a physical object. 14. The non-transitory computer-readable medium of claim 13, wherein the predefined sample size is greater than a number of the plurality of variables. 15. The non-transitory computer-readable medium of claim 1, wherein, after (b) and before (c), the computer-readable instructions further cause the computing device to: initialize a set of iteration support vectors as the defined second set of support vectors; and a predefined number of times, randomly select a fourth set of observation vectors from the training dataset, wherein a number of the fourth set of observation vectors is the predefined sample size; compute a fourth optimal value of the objective function using the selected fourth set of observation vectors to define a fourth set of support vectors, wherein the fourth set of support vectors define a fourth data description for the training dataset; and update the set of iteration support vectors to include the defined fourth set of support vectors; wherein the updated set of iteration support vectors replace the defined second set of support vectors in (c). 16. The non-transitory computer-readable medium of claim 1, wherein the computed value is a number of iterations of (d), and the stop criterion is a predefined maximum number of iterations, wherein the determination is that the stop condition is satisfied when the computed value is greater than or equal to the predefined maximum number of iterations. 17. The non-transitory computer-readable medium of claim 2, wherein the computed value is computed using cp=∥αj−αj-1∥/∥αj-1∥, where αj=Σi=1N αixi where xi are the defined support vectors for each computation, αi is the Lagrange constant of the associated support vector, and N is a number of support vectors included in the defined set of support vectors for each computation, and αj-1=Σi=1Npαipxip where xip are the defined support vectors for a previous computation, αip is the Lagrange constant of the associated previously computed support vector, and Np is a number of support vectors included in the defined set of support vectors for the previous computation, and the stop criterion is a predefined center tolerance value. 18. The non-transitory computer-readable medium of claim 17, wherein the determination is that the stop condition is satisfied when cp≦ε1, where ε1 is the predefined center tolerance value. 19. The non-transitory computer-readable medium of claim 8, wherein the computed value is computed using c p =  R j 2 - R j - 1 2  R j - 1  2 , where Rj2 is the threshold computed using the defined third set of support vectors, and Rj-12 is the threshold computed using the defined first set of support vectors, and the stop criterion is a predefined distance tolerance value. 20. The non-transitory computer-readable medium of claim 19, wherein the determination is that the stop condition is satisfied when cp≦ε1, where ε1 is the predefined distance tolerance value. 21. The non-transitory computer-readable medium of claim 19, wherein a second computed value is computed using cp2=∥αj−αj-1∥/∥αj-1∥, where αj=Σi=1N αixi is computed using the defined third set of support vectors, and αj-1=Σi=1Np αipxip is computed using the defined first set of support vectors, and a second stop criterion is a predefined center tolerance value. 22. The non-transitory computer-readable medium of claim 21, wherein the determination is that the stop condition is satisfied when cp≦ε1, where ε1 is the predefined distance tolerance value, and cp2≦ε2, where ε2 is the predefined center tolerance value. 23. The non-transitory computer-readable medium of claim 1, wherein determining whether or not the stop condition is satisfied comprises: determining a number of consecutive satisfactory comparisons between the computed value and the stop criterion for iterations of (f); and when the determined number of consecutive satisfactory comparisons exceeds a predefined threshold number, the determination is that the stop condition is satisfied. 24. 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. 25. The computing device of claim 24, wherein determining whether or not the stop condition is satisfied comprises: determining a number of consecutive satisfactory comparisons between the computed value and the stop criterion for iterations of (f); and when the determined number of consecutive satisfactory comparisons exceeds a predefined threshold number, the determination is that the stop condition is satisfied. 26. The computing device of claim 24, wherein, after (b) and before (c), the computer-readable instructions further cause the computing device to: initialize a set of iteration support vectors as the defined second set of support vectors; and a predefined number of times, randomly select a fourth set of observation vectors from the training dataset, wherein a number of the fourth set of observation vectors is the predefined sample size; compute a fourth optimal value of the objective function using the selected fourth set of observation vectors to define a fourth set of support vectors, wherein the fourth set of support vectors define a fourth data description for the training dataset; and update the set of iteration support vectors to include the defined fourth set of support vectors; wherein the updated set of iteration support vectors replace the defined second set of support vectors in (c). 27. 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. 28. The method of claim 27, wherein determining whether or not the stop condition is satisfied comprises: determining a number of consecutive satisfactory comparisons between the computed value and the stop criterion for iterations of (f); and when the determined number of consecutive satisfactory comparisons exceeds a predefined threshold number, the determination is that the stop condition is satisfied. 29. The method of claim 27, further comprising, after (b) and before (c): initialize a set of iteration support vectors as the defined second set of support vectors; and a predefined number of times, randomly select a fourth set of observation vectors from the training dataset, wherein a number of the fourth set of observation vectors is the predefined sample size; compute a fourth optimal value of the objective function using the selected fourth set of observation vectors to define a fourth set of support vectors, wherein the fourth set of support vectors define a fourth data description for the training dataset; and update the set of iteration support vectors to include the defined fourth set of support vectors; wherein the updated set of iteration support vectors replace the defined second set of support vectors in (c). 30. The method of claim 27, wherein each observation vector includes a plurality of values, wherein each value of the plurality of values is associated with a variable to define a plurality of variables, wherein each variable of the plurality of variables describes a characteristic of a physical object, wherein the predefined sample size is greater than a number of the plurality of variables.