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title tags authors affiliations date bibliography
ivis: dimensionality reduction in very large datasets using Siamese Networks
dimensionality reduction
unsupervised learning
neural network
name affiliation
Benjamin Szubert
name affiliation orcid
Ignat Drozdov
name index
Bering Limited
18 July 2019


ivis is a dimensionality reduction technique that implements a Siamese Neural Network architecture trained using a novel triplet loss function. Results on simulated and real datasets demonstrate that ivis preserves global data structures in a low-dimensional space and adds new data points to existing embeddings using a parametric mapping function.

ivis is easily integrated into standard machine learning pipelines through a scikit-learn compatible API and scales well to out-of-memory datasets. Both supervised and unsupervised dimensionality reduction modes are supported.

Further information on the algorithm and its application to single cell datasets can be found in [@ivis_scirep]. Implementation of the ivis algorithm is available on GitHub.


This work was supported by funding from the European Commission’s Seventh Framework Programme [FP7-2007-2013] under grant agreement n°HEALTH-F2-2013-602114 (Athero-B-Cell).


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