Robust Localized Multi-view Subspace Clustering
This work addresses noisy data in multi-view clustering for applications like computer vision, though it is incremental as it builds on existing weighting methods.
The paper tackles the problem of noisy samples in multi-view clustering by proposing a localized model that assigns weights to both views and individual samples, achieving robust consensus representation. Experimental results on four benchmarks confirm the model's correctness and effectiveness.
In multi-view clustering, different views may have different confidence levels when learning a consensus representation. Existing methods usually address this by assigning distinctive weights to different views. However, due to noisy nature of real-world applications, the confidence levels of samples in the same view may also vary. Thus considering a unified weight for a view may lead to suboptimal solutions. In this paper, we propose a novel localized multi-view subspace clustering model that considers the confidence levels of both views and samples. By assigning weight to each sample under each view properly, we can obtain a robust consensus representation via fusing the noiseless structures among views and samples. We further develop a regularizer on weight parameters based on the convex conjugacy theory, and samples weights are determined in an adaptive manner. An efficient iterative algorithm is developed with a convergence guarantee. Experimental results on four benchmarks demonstrate the correctness and effectiveness of the proposed model.