Predicting Media Interestingness via Biased Discriminant Embedding and Supervised Manifold Regression

In this paper, we describe our model designed for automatic prediction of media interestingness. Specifically, a two-stage learning framework is proposed. In the first stage, supervised dimensionality reduction is employed to discover the key discriminant information embedded in the original feature space. We present a new algorithm dubbed biased discriminant embedding (BDE) to extract discriminant features with discrete labels and use supervised manifold regression (SMR) to extract discriminant features with continuous labels. In the second stage, SVM is utilized for prediction. Experimental results validate the effectiveness of our approaches.