  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.
