Combining dimensionality reduction with random forests for multi-label classification under interactivity constraints

Learning from multi-label data in an interactive framework is a challenging problem as algorithms must withstand some additional constraints: in particular, learning from few training examples in a limited time. A recent study of multi-label classifier behaviors in this context has identified the potential of the ensemble method “Random Forest of Predictive Clustering Trees” (RF-PCT). However, RF-PCT has shown a degraded performance in terms of computation time for large feature spaces. To overcome this limit, this paper proposes a new hybrid multi-label learning approach IDSR-RF (Independent Dual Space Reduction with RF-PCT) which first reduces the data dimension and then learns a predictive regression model in the reduced spaces with RF-PCT. The feature and the label spaces are independently reduced using the fast matrix factorization algorithm Gravity. The experimental results on nine high-dimensional datasets show that IDSR-RF significantly reduces the computation time without deteriorating the learning performances. To the best of our knowledge, it is currently the most promising learning approach for an interactive multi-label learning system.

This paper describes the MediaEval 2021 Predicting Media Memorability task. After first being proposed at MediaEval 2018, the Predicting Media Memorability task is in its 4th edition this year, as the prediction of short-term and long-term video memorability remains a challenging task. This year, two datasets of videos are used:...

Human character animation is often critical in entertainment content production, including video games, virtual reality or fiction films. To this end, deep neural networks drive most recent advances through deep learning (DL) and deep reinforcement learning (DRL). In this article, we propose a comprehensive survey on the state-of-the-art approaches based...

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