Multivariate Outlier Detection
Pointer to the numeric table with the data for outlier detection. The input can be an object of any class derived from the
Pointer to the numeric table with the vector of means. The input can be an object of any class derived from
Pointer to the numeric table that contains the variance-covariance matrix. The input can be an object of any class derived from
Pointer to the numeric table with the non-negative number that defines the outlier region. The input can be an object of any class derived from
A set of
A numeric table with diagonal elements equal to
1.0and non-diagonal elements equal to
The floating-point type that the algorithm uses for intermediate computations. Can be
Performance-oriented computation method.
Pointer to the numeric table of zeros and ones. Zero in the
i-th position indicates that the
i-th feature vector is an outlier.
By default, the result is an object of the
HomogenNumericTableclass, but you can define the result as an object of any class derived from
- If input data is homogeneous, provide input data and store results in homogeneous numeric tables of the same type as specified in thealgorithmFPTypeclass template parameter.
- For the default outlier detection method (defaultDense), you can benefit from splitting the input data set into blocks for parallel processing.
Product and Performance Information
Performance varies by use, configuration and other factors. Learn more at www.Intel.com/PerformanceIndex.
Notice revision #20201201