Multi-typed Objects Multi-view Multi-instance Multi-label Learning

TL;DR

This paper introduces M4L-JMF, a joint matrix factorization approach for multi-typed objects with diverse instances, views, and labels, outperforming existing methods on complex interconnected data.

Contribution

It proposes a novel joint matrix factorization framework that models multi-typed, multi-view, multi-instance, multi-label data simultaneously, capturing complex interrelations.

Findings

M4L-JMF outperforms existing methods on benchmark datasets.

The approach effectively captures complex interrelations among multi-typed objects.

Experimental results demonstrate significant improvements in predictive accuracy.

Abstract

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.