  Multi-typed objects Multi-view Multi-instance Multi-label Learning (M4L)
deals with interconnected multi-typed objects (or bags) that are made of
diverse instances, represented with heterogeneous feature views and annotated
with a set of non-exclusive but semantically related labels. M4L is more
general and powerful than the typical Multi-view Multi-instance Multi-label
Learning (M3L), which only accommodates single-typed bags and lacks the power
to jointly model the naturally interconnected multi-typed objects in the
physical world. To combat with this novel and challenging learning task, we
develop a joint matrix factorization based solution (M4L-JMF). Particularly,
M4L-JMF firstly encodes the diverse attributes and multiple
inter(intra)-associations among multi-typed bags into respective data matrices,
and then jointly factorizes these matrices into low-rank ones to explore the
composite latent representation of each bag and its instances (if any). In
addition, it incorporates a dispatch and aggregation term to distribute the
labels of bags to individual instances and reversely aggregate the labels of
instances to their affiliated bags in a coherent manner. Experimental results
on benchmark datasets show that M4L-JMF achieves significantly better results
than simple adaptions of existing M3L solutions on this novel problem.
