Learning Latent Factors in Linked Multi-modality Data

Tiantian He, Keith C. C. Chan

Abstract: Many real-world data can be represented as networks in which the vertices and edges represent data entities and the interrelationship between them, respectively. The discovery of network clusters, which are typical latent structures, is one of the most significant tasks of network analytics. Currently, there are no effective approaches that are able to deal with linked data with features from multimodality. To address it, we propose an effective model for learning latent factors in linked multimodality data, named as LFLMD. Given the link structure and multimodality features associated with vertices, LFLMD formulates a constrained optimization problem to learn corresponding latent spaces representing the strength that each vertex belongs to the latent components w.r.t. link structure and multimodality features. Besides, LFLMD further adopts an effective method to model the affinity between pairwise vertices so that the cluster membership for each vertex can be revealed by grouping vertices sharing more similar latent structures. For model inference, a series of iterative algorithms for updating the variables in the latent spaces are derived. LFLMD has been tested on several sets of networked data with different modalities of features and it is found LFLMD is very effective.

External IDs:dblp:conf/ismis/HeC18