Exploiting Low-Rank Latent Gaussian Graphical Model Estimation for Visual Sentiment Distributions

Currently, an increasing number of applications and services has encouraged users to openly express their emotions via images. Unlike visual sentiment classification, visual sentiment distribution learning exploits the overall distribution to represent the relative importance of sentiment labels. Considering that most relevant studies have failed to completely model correlation structures or explicitly apply them to unknown instances, in this paper, we proposed a low-rank latent Gaussian graphical model estimation (LGGME) method for visual sentiment distribution learning tasks. There are three main characteristics of LGGME: 1) an integrated inverse covariance matrix whose parameters characterize the latent correlation structures between and within features and sentiments is estimated based on the sparse Gaussian graphical model; 2) a multivariate normal assumption is assigned on the concatenated latent feature representations and the estimated sentiment distributions instead of the original observations for a reasonable surrogate; and 3) the latent feature representations are projected from a low-rank subspace, which is also available for unseen instances, and the estimated sentiment distributions are evaluated by KL divergence to ensure a suitable setting for distribution learning. We further developed an effective optimization algorithm based on the alternating direction method of multipliers (ADMM) for our objective function. The experimental results obtained on three publicly available datasets demonstrate the superiority of our proposed method.