Self-Guided Partial Graph Propagation for Incomplete Multiview Clustering
Research output: Journal Publications and Reviews › RGC 21 - Publication in refereed journal › peer-review
Author(s)
Related Research Unit(s)
Detail(s)
Original language | English |
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Pages (from-to) | 10803-10816 |
Journal / Publication | IEEE Transactions on Neural Networks and Learning Systems |
Volume | 35 |
Issue number | 8 |
Online published | 3 Mar 2023 |
Publication status | Published - Aug 2024 |
Link(s)
Abstract
In this work, we study a more realistic challenging scenario in multiview clustering (MVC), referred to as incomplete MVC (IMVC) where some instances in certain views are missing. The key to IMVC is how to adequately exploit complementary and consistency information under the incompleteness of data. However, most existing methods address the incompleteness problem at the instance level and they require sufficient information to perform data recovery. In this work, we develop a new approach to facilitate IMVC based on the graph propagation perspective. Specifically, a partial graph is used to describe the similarity of samples for incomplete views, such that the issue of missing instances can be translated into the missing entries of the partial graph. In this way, a common graph can be adaptively learned to self-guide the propagation process by exploiting the consistency information, and the propagated graph of each view is in turn used to refine the common self-guided graph in an iterative manner. Thus, the associated missing entries can be inferred through graph propagation by exploiting the consistency information across all views. On the other hand, existing approaches focus on the consistency structure only, and the complementary information has not been sufficiently exploited due to the data incompleteness issue. By contrast, under the proposed graph propagation framework, an exclusive regularization term can be naturally adopted to exploit the complementary information in our method. Extensive experiments demonstrate the effectiveness of the proposed method in comparison with state-of-the-art methods. The source code of our method is available at the https://github.com/CLiu272/TNNLS-PGP. © 2023 IEEE.
Research Area(s)
- Matrix decomposition, Indexes, Computer science, Task analysis, Silicon, Learning systems, Laplace equations, Graph propagation, incomplete multiview clustering (IMVC), FUSION
Citation Format(s)
Self-Guided Partial Graph Propagation for Incomplete Multiview Clustering. / Liu, Cheng; Li, Rui; Wu, Si et al.
In: IEEE Transactions on Neural Networks and Learning Systems, Vol. 35, No. 8, 08.2024, p. 10803-10816.
In: IEEE Transactions on Neural Networks and Learning Systems, Vol. 35, No. 8, 08.2024, p. 10803-10816.
Research output: Journal Publications and Reviews › RGC 21 - Publication in refereed journal › peer-review