N$^3$LARS: Minimum Redundancy Maximum Relevance Feature Selection for Large and High-dimensional Data
This method addresses feature selection challenges in big data domains like biology, offering a scalable solution, but it is incremental as it builds on existing LARS and HSIC techniques.
The authors tackled feature selection for large, high-dimensional data by proposing N^3LARS, a nonlinear extension of LARS using HSIC, which achieved efficient selection through distributed computing and convex optimization, with effectiveness demonstrated on classification, regression, and a biology dataset.
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. Moreover, N${}^3$LARS is a convex method and can find a global optimum solution. The effectiveness of the proposed method is first demonstrated through feature selection experiments for classification and regression with small and high-dimensional datasets. Finally, we evaluate our proposed method over a large and high-dimensional biology dataset.