Title: RIPML: A Restricted Isometry Property based Approach to Multilabel Learning,
Abstract: The multilabel learning problem with large number of labels, features, and
data-points has generated a tremendous interest recently. A recurring theme of
these problems is that only a few labels are active in any given datapoint as
compared to the total number of labels. However, only a small number of
existing work take direct advantage of this inherent extreme sparsity in the
label space. By the virtue of Restricted Isometry Property (RIP), satisfied by
many random ensembles, we propose a novel procedure for multilabel learning
known as RIPML. During the training phase, in RIPML, labels are projected onto
a random low-dimensional subspace followed by solving a least-square problem in
this subspace. Inference is done by a k-nearest neighbor (kNN) based approach.
We demonstrate the effectiveness of RIPML by conducting extensive simulations
and comparing results with the state-of-the-art linear dimensionality reduction
based approaches.