Abstract
In many practical problems, the same objects can be described in many different ways or from different angles. These multiple descriptions constitute multiple views of objects. Multi-view classification methods try to exploit information from all views to improve the classification performance and reduce the effect of noises. However, how to efficiently exploit the consistency and specificity in multiple views remains a challenge. In addition, it is also worth to explore the processing results of multi-view data more inline with human cognition. For this reason, we propose a new multi-view classification algorithm, Consistent and Specific Multi-View Relative-transform Classification (CSMRtC). CSMRtC firstly explores the underlying subspace structure of different views exhaustively, to evacuate consistency and specificity of multi-view data. Next, these data matrices are processed using the relative transform technique. As for the consistency and specificity, consistent matrix stores the shared information of multiple data matrices, specificity captures the characteristic of each view. Then, we use the relative transformations to transform data from raw space to relative spaces, to achieve the purpose of suppress noise in the data and improve the distinction between the data. Comprehensive evaluations with several state-of-the-art competitors demonstrate the efficiency and the superiority of the proposed method.
Supported by NSFC (61872300 and 61873214), Fundamental Research Funds for the Central Universities (XDJK2019B024), NSF of CQ CSTC (cstc2018jcyjAX0228).
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Ping, S., Zhang, L., Wang, X., Yu, G. (2019). Consistent and Specific Multi-view Relative-Transform Classification. In: Zeng, A., Pan, D., Hao, T., Zhang, D., Shi, Y., Song, X. (eds) Human Brain and Artificial Intelligence. HBAI 2019. Communications in Computer and Information Science, vol 1072. Springer, Singapore. https://doi.org/10.1007/978-981-15-1398-5_20
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DOI: https://doi.org/10.1007/978-981-15-1398-5_20
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