Align the features across multiple datasets into a common feature space.
This is a three-level algorithm. In the first level, a series of input datasets is projected into a common feature space using a configurable mapper. The common space is initially defined by a chosen exemplar from the list of input datasets, but is subsequently refined by iteratively combining the common space with the projected input datasets.
In the second (optional) level, the original input datasets are again aligned with (or projected into) the intermediate first-level common space. Through a configurable number of iterations the common space is further refined by repeated projections of the input datasets and combination/aggregation of these projections into an updated common space.
In the third level, the input datasets are again aligned with the, now final, common feature space. The output of this algorithm are trained mappers (one for each input dataset) that transform the individual features spaces into the common space.
Level 1 and 2 are performed by the train() method, and level 3 is performed when the trained Hyperalignment instance is called with a list of datasets. This dataset list may or may not be identical to the training datasets.
The default values for the parameters of the algorithm (e.g. projection via Procrustean transformation, common space aggregation by averaging) resemble the setup reported in Haxby et al., Neuron (2011) A common, high-dimensional model of the representational space in human ventral temporal cortex.
Notes
Available conditional attributes:
(Conditional attributes enabled by default suffixed with +)
Examples
>>> # get some example data
>>> from mvpa2.testing.datasets import datasets
>>> from mvpa2.misc.data_generators import random_affine_transformation
>>> ds4l = datasets['uni4large']
>>> # generate a number of distorted variants of this data
>>> dss = [random_affine_transformation(ds4l) for i in xrange(4)]
>>> ha = Hyperalignment()
>>> ha.train(dss)
>>> mappers = ha(dss)
>>> len(mappers)
4
Methods
reset() | |
train(datasets) | Derive a common feature space from a series of datasets. |
Initialize instance of Hyperalignment
Parameters: | alignment :
alpha : float, optional
level2_niter : int, optional
ref_ds : int or None, optional zscore_all : bool, optional
zscore_common : bool, optional
combiner1 :
combiner2 :
enable_ca : None or list of str
disable_ca : None or list of str
descr : str
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Methods
reset() | |
train(datasets) | Derive a common feature space from a series of datasets. |
Derive a common feature space from a series of datasets.
Parameters: | datasets : sequence of datasets |
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Returns: | A list of trained Mappers matching the number of input datasets. : |