Demo i-tSNE

"Interactive dimensionality reduction of large datasets using interpolation" (demo version for paper submitted to ESANN 2018 conference).

Abstract: In this work we present an approach to achieve interactive dimensionality reduction (iDR) on large datasets. The main idea of the paper relies on using regression neural network (GRNN) interpolation to obtain massive out of sample projections from iDR projections obtained on a reduced sample of the original dataset. The proposed method allows to achieve fluid iDR interaction on datasets between 45x and 100x larger than with the original DR method for similar latencies, yet achieving good distance preservation. The paper includes a rank-based comparison between the proposed method and the DR method used alone for different datasets and parameter values.
Authors: Ignacio Díaz, Daniel Pérez, Abel A. Cuadrado, Diego García, Manuel Domínguez

Credits:

• Built in HTML5 canvas using P5js.
• This application uses a slightly modified version of tsnejs by Andrej Karpathy.
• Parts of the application were inspired by Wattenberg, et al., "How to Use t-SNE Effectively", Distill, 2016.

by GSDPI research team