FastICA¶
Independent component analysis using the FastICA method.
ICA will produce components, that is linear combinations of input features, such that the values in the different output features are maximally statistically independent from each other (that is, the samples in of the dimensions share little information with samples in another feature. In contrast to PCA, this method is usually not used for dimensionality reduction, but for identifying independent features in the data. Such features can be assumed to be more interpretable than other projections, since they are likely to relate to independent underlying processes that generated the data. As such, these are features that can be highly useful in subsequent processing stages, for instance non-linear feature extraction or sparse machine learning techniques. One weakness of ICA is that it is quite sensitive to outliers in the data (and to a lesser extent to noise sources), resulting in brittle or bad solutions when the input data is not already reasonably clean. 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
max_iter¶
Maximum number of iterations. This is one of the stopping criteria to limit the compute time. The default is usually fine, and gains from increasing the number of iterations will be minimal (it can be worth experimenting with lower iteration numbers if the algorithm must finish in a fixed time budget, at a cost of potentially less accurate solutions).
- verbose name: Maximum Number Of Iterations
- default value: 750
- port type: IntPort
- value type: int (can be None)
num_components¶
Number of components to keep. If left unspecified, all components are kept, that is, the number of output features will correspond to the number of input dimensions.
- verbose name: Number Of Components
- default value: None
- port type: IntPort
- value type: int (can be None)
algorithm¶
Optimization approach. For highly noisy data the parallel approach is usually better, but can be computationally somewhat more costly. Note that this does not mean that multiple cores are being used, but instead multiple components are optimized simultaneously.
- verbose name: Algorithm
- default value: parallel
- port type: EnumPort
- value type: str (can be None)
whiten¶
Pre-whiten the data. If disabled, the data must have been whitened otherwise before.
- verbose name: Pre-Whiten
- default value: True
- port type: BoolPort
- value type: bool (can be None)
fun¶
Non-linearity to use. The logcosh function corresponds to Infomax ICA, which yields the best quality, but other non-linearities, particularly cube, are faster to calculate.
- verbose name: Non-Linearity To Use
- default value: logcosh
- port type: EnumPort
- value type: str (can be None)
tolerance¶
Convergence tolerance. This is the desired errors tolerance or acceptable inaccuracy in the solution. Using larger values gives less accurate results, but will lead to faster compute times. Note that, for biosignal-driven machine learning systems, one often does not need very small tolerances.
- verbose name: Tolerance
- default value: 0.0001
- port type: FloatPort
- value type: float (can be None)
random_seed¶
Random seed. Different values may yield slightly different results.
- verbose name: Random Seed
- default value: 12345
- port type: IntPort
- value type: int (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)