Low-Rank Regularized Multi-Representation Learning for Fashion Compatibility Prediction

The currently flourishing fashion-oriented community websites and the continuous pursuit of fashion have attracted the increased research interest of the fashion analysis community. Many studies show that predicting the compatibility of fashion outfits is a nontrivial task due to the difficulty in capturing the implicit patterns affecting fashion compatibility prediction and the complex relationships presented by raw data. To address these problems, in this paper, we propose a transductive low-rank hypergraph regularizer multiple-representation learning framework (LHMRL), whereby we formulate the processes of feature representation and fashion compatibility prediction in a joint framework. Specifically, we first introduce a low-rank regularized multiple-representation learning framework, in which the lowest-rank multiple representations of samples can be learned to characterize samples from different perspectives. In this framework, we maximize the total difference among multiple representations based on Grassmann manifold theory and incorporate a common hypergraph regularizer to naturally encode the complex relationships between fashion items and an outfit. To enhance the representation ability of our model, we then develop a supervised learning term by exploiting two types of supervision information from labeled data. Experiments on a publicly available large-scale dataset demonstrate the effectiveness of our proposed model over the state-of-the-art methods.