  Although significant progress achieved, multi-label classification is still
challenging due to the complexity of correlations among different labels.
Furthermore, modeling the relationships between input and some (dull) classes
further increases the difficulty of accurately predicting all possible labels.
In this work, we propose to select a small subset of labels as landmarks which
are easy to predict according to input (predictable) and can well recover the
other possible labels (representative). Different from existing methods which
separate the landmark selection and landmark prediction in the 2-step manner,
the proposed algorithm, termed Selecting Predictable Landmarks for Multi-Label
Learning (SPL-MLL), jointly conducts landmark selection, landmark prediction,
and label recovery in a unified framework, to ensure both the
representativeness and predictableness for selected landmarks. We employ the
Alternating Direction Method (ADM) to solve our problem. Empirical studies on
real-world datasets show that our method achieves superior classification
performance over other state-of-the-art methods.
