Algorithm Input

The multivariate BACON outlier detection algorithm accepts the input described below. Pass the Input ID as a parameter to the methods that provide input for your algorithm. For more details, see Algorithms.

Input ID

Input

data

Pointer to the n x p numeric table with the data for outlier detection. The input can be an object of any class derived from the NumericTable class.

Algorithm Parameters

The multivariate outlier detection algorithm has the following parameters, which depend on the computation method parameter method:

Parameter

Default Value

Description

algorithmFPType

float

The floating-point type that the algorithm uses for intermediate computations. Can be float or double.

initializationMethod

baconMedian

The initialization method, can be:

  • baconMedian - median-based method
  • defaultDense - Mahalanobis distance-based method

alpha

0.05

One-tailed probability that defines the (1 - α) quantile of the χ 2 distribution with p degrees of freedom.

Recommended value: α/n, where n is the number of observations.

toleranceToConverge

0.005

The stopping criterion. The algorithm is terminated if the size of the basic subset is changed by less than the threshold.

Algorithm Output

The multivariate BACON outlier detection algorithm calculates the result described below. Pass the Result ID as a parameter to the methods that access the results of your algorithm. For more details, see Algorithms.

Result ID

Result

weights

Pointer to the n x 1 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 HomogenNumericTable class, but you can define the result as an object of any class derived from NumericTable except the PackedSymmetricMatrix, PackedTriangularMatrix, and CSRNumericTable.

Para obter informações mais completas sobre otimizações do compilador, consulte nosso aviso de otimização.
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