Coupled Dictionary Learning for Multi-label Embedding

• Main Authors: Niu, Sijia, Xu, Qian, Zhu, Pengfei, Hu, Qinghua, Shi, Hong
• Format: Conference Proceeding
• Language: English
• Subjects: Correlation; Dictionaries; Dimensionality reduction; Optimization; Task analysis; Training
• ISSN: 2161-4407
• DOI: 10.1109/IJCNN.2019.8852201

With the booming of social networks, such as Facebook and Flickr, the candidate labels of an instance can be numerous. Hence, traditional multi-label learning algorithms are out of capability to handle a large quantity of labels for the unaffordable time complexity. To alleviate this problem, label space dimension reduction (LSDR) is proposed by transforming the original label space into a lower dimensional one. Inspired by the effectiveness of coupled dictionary learning (CDL) in dealing with cross-modal data, in this paper, we proposed a novel algorithm named Coupled Dictionary Learning for Multi-label Embedding (ML-CDL) to track the problem of LSDR. We novelly treat feature and label as coupled domains. Then CDL is utilized to generate the low-dimensional latent space that leverages the information between feature and label spaces. In particular, the sparse representation coefficients embody the properties of interpretability, discriminability and sparsity. Experimental results on benchmark datasets demonstrate the effectiveness of our algorithm.