shagunsodhani

Visualizing Data using t-SNE

Introduction

• Method to visualize high-dimensional data points in 2/3 dimensional space.
• Data visualization techniques like Chernoff faces and graph approaches just provide a representation and not an interpretation.
• Dimensionality reduction techniques fail to retain both local and global structure of the data simultaneously. For example, PCA and MDS are linear techniques and fail on data lying on a non-linear manifold.
• t-SNE approach converts data into a matrix of pairwise similarities and visualizes this matrix.
• Based on SNE (Stochastic Neighbor Embedding)
• Link to paper

deroneriksson

Deron Eriksson SystemML Git Notes

Useful Links

• Luciano's Development Guidelines - https://github.com/SparkTC/development-guidelines
• Mike's SystemML Git Guide - https://gist.github.com/dusenberrymw/78eb31b101c1b1b236e5
• Dev Mailing List - http://mail-archives.apache.org/mod_mbox/systemml-dev/
• SystemML Website - http://systemml.apache.org/
• SystemML on GitHub - https://github.com/apache/systemml
• SystemML Documentation - https://apache.github.io/systemml
• Scott Chacon's Pro Git - https://progit.org/