Developer Reference

Contents

vslSSEditOutliersDetection

Modifies array pointers related to multivariate outliers detection.

Syntax

status
=
vslsSSEditOutliersDetection
(
task
,
nparams
,
params
,
w
);
status
=
vsldSSEditOutliersDetection
(
task
,
nparams
,
params
,
w
);
Include Files
  • mkl.h
Input Parameters
Name
Type
Description
task
VSLSSTaskPtr
Descriptor of the task
nparams
const MKL_INT*
Pointer to the number of method parameters
params
const float*
for
vslsSSEditOutliersDetection
const double*
for
vsldSSEditOutliersDetection
Pointer to the array of method parameters
w
float*
for
vslsSSEditOutliersDetection
double*
for
vsldSSEditOutliersDetection
Pointer to an array of size
n
. The array holds the weights of observations to be marked as outliers.
Output Parameters
Name
Type
Description
status
int
Current status of the task
Description
The
vslSSEditOutliersDetection
routine uses the parameters passed to replace
  • the pointers to the number of method parameters and to the array of the method parameters of size
    nparams
  • the pointer to the array that holds the calculated weights of the observations
If you pass a value of
NULL
for a specific input parameter, the value of that parameter in the task descriptor is unchanged.
Intel® MKL
provides the BACON algorithm ([ Billor00 ]) for the detection of multivariate outliers. Pack the parameters of the BACON algorithm into the
params
array and pass them into the editor. Table
"Structure of the Array of BACON Parameters"
describes the
params
structure.
Structure of the Array of BACON Parameters
Array Position
Algorithm Parameter
Description
0
Method to start the algorithm
The parameter takes one of the following possible values:
VSL_SS_METHOD_BACON_MEDIAN_INIT
, if the algorithm is started using the median estimate. This is the default value of the parameter.
VSL_SS_METHOD_BACON_MAHALANOBIS_INIT
, if the algorithm is started using the Mahalanobis distances.
1
α
One-tailed probability that defines the
(1 -
α
) quantile of
χ
2
distribution with
p
degrees of freedom. The recommended value is
α
/
n
, where
n
is the number of observations. By default, the value is 0.05.
2
δ
Stopping criterion; the algorithm is terminated if the size of the basic subset is changed less than
δ
. By default, the value is 0.005.
Output of the algorithm is the vector of weights,
BaconWeights
, such that
BaconWeights
(
i
) = 0 if
i
-th observation is detected as an outlier. Otherwise
BaconWeights
(
i
) =
w
(
i
), where
w
is the vector of input weights. If you do not provide the vector of input weights,
BaconWeights
(
i
) is set to 1 if the
i
-th observation is not detected as an outlier.
See additional details about usage model of the algorithm in the
Intel® MKL
Summary Statistics Application Notes
document [ SS Notes ].

Product and Performance Information

1

Intel's compilers may or may not optimize to the same degree for non-Intel microprocessors for optimizations that are not unique to Intel microprocessors. These optimizations include SSE2, SSE3, and SSSE3 instruction sets and other optimizations. Intel does not guarantee the availability, functionality, or effectiveness of any optimization on microprocessors not manufactured by Intel. Microprocessor-dependent optimizations in this product are intended for use with Intel microprocessors. Certain optimizations not specific to Intel microarchitecture are reserved for Intel microprocessors. Please refer to the applicable product User and Reference Guides for more information regarding the specific instruction sets covered by this notice.

Notice revision #20110804