Learning Deep Latent Spaces for Multi-Label Classification

Multi-label classification is a practical yet challenging task in machine learning related fields, since it requires the prediction of more than one label category for each input instance. We propose a novel deep neural networks (DNN) based model, Canonical Correlated AutoEncoder (C2AE), for solving this task. Aiming at better relating feature and label domain data for improved classification, we uniquely perform joint feature and label embedding by deriving a deep latent space, followed by the introduction of label-correlation sensitive loss function for recovering the predicted label outputs. Our C2AE is achieved by integrating the DNN architectures of canonical correlation analysis and autoencoder, which allows end-to-end learning and prediction with the ability to exploit label dependency. Moreover, our C2AE can be easily extended to address the learning problem with missing labels. Our experiments on multiple datasets with different scales confirm the effectiveness and robustness of our proposed method, which is shown to perform favorably against state-of-the-art methods for multi-label classification.Comment: published in AAAI-201

Multi-label classification with feature-aware non-linear label space transformation

Multi-label classification with many classes has recently drawn a lot of attention. Existing methods address this problem by performing linear label space transformation to reduce the dimension of label space, and then conducting independent regression for each reduced label dimension. These methods however do not capture nonlinear correlations of the multiple labels and may lead to significant information loss in the process of label space reduction. In this paper, we first propose to exploit kernel canonical correlation analysis (KCCA) to capture nonlinear label correlation information and perform nonlinear label space reduction. Then we develop a novel label space reduction method that explicitly combines linear and nonlinear label space transformations based on CCA and KCCA respectively to address multi-label classification with many classes. The proposed method is a feature-aware label transformation method that promotes the label predictability in the transformed label space from the input features. We conduct experiments on a number of multi-label classification datasets. The proposed approach demonstrates good performance, comparing to a number of state-of-the-art label dimension reduction methods