  With the advent of big data across multiple high-impact applications, we are
often facing the challenge of complex heterogeneity. The newly collected data
usually consist of multiple modalities and are characterized with multiple
labels, thus exhibiting the co-existence of multiple types of heterogeneity.
Although state-of-the-art techniques are good at modeling complex heterogeneity
with sufficient label information, such label information can be quite
expensive to obtain in real applications. Recently, researchers pay great
attention to contrastive learning due to its prominent performance by utilizing
rich unlabeled data. However, existing work on contrastive learning is not able
to address the problem of false negative pairs, i.e., some `negative' pairs may
have similar representations if they have the same label. To overcome the
issues, in this paper, we propose a unified heterogeneous learning framework,
which combines both the weighted unsupervised contrastive loss and the weighted
supervised contrastive loss to model multiple types of heterogeneity. We first
provide a theoretical analysis showing that the vanilla contrastive learning
loss easily leads to the sub-optimal solution in the presence of false negative
pairs, whereas the proposed weighted loss could automatically adjust the weight
based on the similarity of the learned representations to mitigate this issue.
Experimental results on real-world data sets demonstrate the effectiveness and
the efficiency of the proposed framework modeling multiple types of
heterogeneity.
