Multi-Target Regression via Robust Low-Rank Learning

• Published in: IEEE transactions on pattern analysis and machine intelligence Vol. 40; no. 2; pp. 497 - 504
• Main Authors: Zhen, Xiantong, Yu, Mengyang, He, Xiaofei, Li, Shuo
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.02.2018; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Biomedical imaging; Computational modeling; Computer vision; Correlation; Data mining; Data models; Image analysis; Input output; Kernel; Kernels; Learning; matrix elastic nets; Medical imaging; Modelling; multi-layer learning; multi-target regression; Multilayers; Optimization; Regression analysis; Robust low-rank learning; Robustness; Robustness (mathematics); State of the art
• DOI: 10.1109/TPAMI.2017.2688363
• ISSN: 0162-8828; 1939-3539

Multi-target regression has recently regained great popularity due to its capability of simultaneously learning multiple relevant regression tasks and its wide applications in data mining, computer vision and medical image analysis, while great challenges arise from jointly handling inter-target correlations and input-output relationships. In this paper, we propose Multi-layer Multi-target Regression (MMR) which enables simultaneously modeling intrinsic inter-target correlations and nonlinear input-output relationships in a general framework via robust low-rank learning. Specifically, the MMR can explicitly encode inter-target correlations in a structure matrix by matrix elastic nets (MEN); the MMR can work in conjunction with the kernel trick to effectively disentangle highly complex nonlinear input-output relationships; the MMR can be efficiently solved by a new alternating optimization algorithm with guaranteed convergence. The MMR leverages the strength of kernel methods for nonlinear feature learning and the structural advantage of multi-layer learning architectures for inter-target correlation modeling. More importantly, it offers a new multi-layer learning paradigm for multi-target regression which is endowed with high generality, flexibility and expressive ability. Extensive experimental evaluation on 18 diverse real-world datasets demonstrates that our MMR can achieve consistently high performance and outperforms representative state-of-the-art algorithms, which shows its great effectiveness and generality for multivariate prediction.