Metadata-Version: 2.1
Name: lsmmdma
Version: 0.0.1
Summary: Scaling MMD-MA.
Home-page: https://github.com/google-research/large_scale_mmdma
Author: Google LLC
Author-email: lpapaxanthos@google.com
License: Apache 2.0
Description: This is not an officially supported Google product.
        
        In MMD-MA ([Liu et al. 2019, Jointly Embedding Multiple
        Single-Cell Omics Measurements](https://pubmed.ncbi.nlm.nih.gov/34632462/)), we project two sets of points, from two different spaces endowed
        with a positive definite kernel, to a shared Euclidean space of dimension
        `low_dim`. The mappings from high to low dimensional space are
        obtained using functions belonging to the respective RKHS. To obtain the
        mappings, we minimise a loss function that is composed of three terms:
        - an MMD term between the low dimensional representations of the two views,
        which encourages them to have the same distribution.
        - two non-collapsing penalty terms (corresponding to the `pen_dual` or
        `pen_primal` functions), one for each view. These terms ensure that
        the low dimensional representations are mutually orthogonal, preventing
        collapsing.
        - two distortion penalties (corresponding to the `dis_dual` or
        `dis_primal` functions), one for each view. These terms encourage the
        low dimensional representation to obtain the same pairwise structure as
        the original views.
        
        MMD-MA can be formulated using either the primal (when we use the linear
        kernel in the input spaces) or the dual problem. Each has
        advantages or disadvantages depending on the input data. In each view, when the
        number of features is larger than the number of samples
        p_featureX >> n_sampleX, then the dual formulation is beneficial in terms
        of runtime and memory, while if n_sampleX >> p_sampleX, the primal
        formulation is favorable.
        
        ## Example
        
        Work in progress. See main.py for usage.
Keywords: kernel,data integration
Platform: UNKNOWN
Classifier: Intended Audience :: Science/Research
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: Apache Software License
Description-Content-Type: text/markdown
