In many machine learning applications, feature extraction is a key step prior to the application of learning methods. Shape analytics offers tools to generate geometric featurizations of data for use in data analysis and machine learning pipelines. Topological data analysis (TDA) approaches supplement traditional data analysis approaches, extracting underlying patterns in time series data, images, and other high-dimensional data sets. Although there are certainly machine learning methodologies that can work directly from raw data to generate shape descriptors, our shape analytics toolkit allows us to address several issues that are not currently handled by these strategies.
- How do we turn non-traditional problems with shape into traditional machine learning problems?
- How do we knowingly generate and use specific, interpretable shape features?
- Can we generate low-dimensional features to avoid the “curse of dimensionality”?
- Can we offer any robustness guarantees for our featurization?