Anthony Bak, Principal Data Scientist at Ayasdi at MLconf SEA - 5/01/15
Topology as Framework for Data Science: Ayasdi has a unique approach to machine learning and data analysis using topology. This framework represents a revolutionary way to look at and understand data that is orthogonal but complementary to traditional machine learning and statistical tools. In this presentation I will show you what is meant by this statement: How does topology help with data analysis? Why would you use topology? I will illustrate with both synthetic examples and problems we’ve solved for our clients.
1. Topology of shape doesn’t depend on the coordinates used to
describe the shape
1. Different feature sets can describe the same phenomena
1. While processing data, we frequently alter coordinates: scaling,
You want to study properties of your data that are invariant
under coordinate changes
• Topological features don’t change when you stretch and distort the
Advantage: Makes problems easier
Less pre-processing of data
Robust (stable) data
• Replace the metric space with a combinatorial summary: a simplicial
• Data becomes easier to manage, search, and query while
maintaining essential features.
• Leverages many known algorithms from graph theory, computational
topology, computational geometry.