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WhiteningTransform

Whiten (decorrelate and normalize) the given data without rotation.

A whitening transformation can be used to remove correlations between features. In addition, the features will also be normalized to unit variance, and optionally centered to zero mean. Whitening is a comprehensive type of normalization that can be used as pre-processing for a wide variety of subsequent processing stages. Important:This node is adaptive to the data, that is, it will learn a transformation of the data that depends on the input data. In order to learn this transformation, the node requires a reasonable amount of input data for calibration or "training" (otherwise it will yield an ill-fitting or noisy model). Since this feature extraction method is not capable of being trained incrementally on streaming data, the method requires a data packet that contains the entire training data; this training data packet can either be accumulated online and then released in one shot using the Accumulate Calibration Data node, or it can be imported from a separate calibration recording and then spliced into the processing pipeline using the Inject Calibration Data, where it passes through the same nodes as the regular data until it reaches the machine learning node, where it is used for calibration. Once this node is calibrated, the trainable state of this node can be saved to a model file and later loaded for continued use. Like most other feature extraction nodes, this node can compute features between elements of an axis of your choice while treating elements of another axis as the observations, trials, or samples. It can also optionally compute multiple separate models on different slices of the data along some axis of choice. It is also possible to pool multiple axes for any of these roles. More Info... Version 1.0.0

Ports/Properties

domain_axes

Axes which form the input domain of the transformation. Features are computed between elements along these axes (or in other words, elements along these axes will be combined with each other to yield features). This is a comma-separated list of axis names (for example: "space, frequency"), possibly empty, or the special string "(all others)", which stands for all axes that are not listed in the other two lists of axes. For time-series data, this is usually the space axis, and if features have already been extracted from the data through some other method, it would be the features axis. In rare cases it can also include other axes, such as frequency, lag, and time. This axis drops out of the data and is replaced by a feature axis that has a number of elements that corresponds to the output feature dimension of the model.

  • verbose name: Compute Features Between Axes
  • default value: (all others)
  • port type: ComboPort
  • value type: str (can be None)

aggregate_axes

Axes that have the statistical observations in them. The elements along these axes are treated as the "trials", "samples", or, equivalently, "observations". Adaptive feature extractors will aggregate statistics along the elements of these axes during training. When the transformer applies to data, this axis remains in the data (if multiple, they will be flattened into one axis, however). This is a comma-separated list of axis names (for example: "time, instance"), possibly empty, or the special string "(all others)", which stands for all axes that are not listed in the other two lists of axes. This is almost always the instance axis (especially if the data has already been segmented, i.e., if the Segmentation node was used), but in some cases it may also be the time axis, or occasionally other axes.

  • verbose name: Treat Elements As Trials/samples Along Axes
  • default value: instance
  • port type: ComboPort
  • value type: str (can be None)

separate_axes

Axes along which to learn separate models. It is possible to use multiple separate feature-extraction models, each of which operates on a different slice of the data. This node does not combine data between elements along these axes in any way (although features between these elements may of course be combined in later stages, for instance in a classifier node). This is a comma-separated list of axis names (for example: "time, frequency"), possibly empty, or the special string "(all others)", which stands for all axes that are not listed in the other two lists of axes. This axis, if any, will also persist in the data.

  • verbose name: Compute Separate Models Along Axes
  • default value:
  • port type: ComboPort
  • value type: str (can be None)

data

Data to process.

  • verbose name: Data
  • default value: None
  • port type: DataPort
  • value type: Packet (can be None)
  • data direction: INOUT

shrinkage

Regularization strength. This is primarily to prevent degenerate solutions.

  • verbose name: Regularization Strength
  • default value: 0.01
  • port type: FloatPort
  • value type: float (can be None)

center

Center data before whitening. This will remove the mean.

  • verbose name: Center Data
  • default value: True
  • port type: BoolPort
  • value type: bool (can be None)

decorrelate

Decorrelate the data. If disabled, only normalization will be performed.

  • verbose name: Decorrelate Data
  • default value: True
  • port type: BoolPort
  • value type: bool (can be None)

retain_axes

Retain original axes. If disabled, the domain axes will be replaced by a feature axis.

  • verbose name: Retain Axes
  • default value: True
  • port type: BoolPort
  • value type: bool (can be None)

set_breakpoint

Set a breakpoint on this node. If this is enabled, your debugger (if one is attached) will trigger a breakpoint.

  • verbose name: Set Breakpoint (Debug Only)
  • default value: False
  • port type: BoolPort
  • value type: bool (can be None)

metadata

User-definable meta-data associated with the node. Usually reserved for technical purposes.

  • verbose name: Metadata
  • default value: {}
  • port type: DictPort
  • value type: dict (can be None)