N$^3$LARS: Minimum Redundancy Maximum Relevance Feature Selection for Large and High-dimensional Data

Understand

We propose a feature selection method that finds non-redundant features from a large and high-dimensional data in nonlinear way.

• Specifically, we propose a nonlinear extension of the non-negative least-angle regression (LARS) called N${}^3$LARS, where the similarity between input and output is measured through the normalized version of the Hilbert-Schmidt Independence Criterion (HSIC).
• An advantage of N${}^3$LARS is that it can easily incorporate with map-reduce frameworks such as Hadoop and Spark.
• Thus, with the help of distributed computing, a set of features can be efficiently selected from a large and high-dimensional data.