Streaming t-SNE

Start: 10/09/2017 - 4:15pm
End  : 10/09/2017 - 5:15pm

Applied Math Seminar

Mariam Salloum (CMC)


Data sets involving many dimensions are commonplace in scientific computing and machine learning, however, the human visual system is limited to interpreting data in only two or three dimensions. An award-wining technique called Student-T Stochastic Neighbor Embedding (t-SNE) has been presented to embed data of high dimension into a 2-D or 3-D space, while preserving local relationships in the original data. This has yielded marked improvements in the quality of the embedding, especially with regard to its ability to preserve clustering and structure. However, the original t-SNE can only be used in batch mode, i.e. all data must be available prior to the start of the algorithm. It is therefore compelling to have a version of t-SNE which can accommodate a streaming operational mode, i.e. to incorporate new information into the embedded representation as it arrives. Such a system would allow an operator to observe changes in complex, high dimensional data in near real-time. In this work, we present a streaming t-SNE variation and provide visualizations with streaming data.

Emmy Noether Rm, Millikan 1021, Pomona College

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