![]() ![]() ![]() We then design an alternate updating algorithm to learn a unified clustering decision that can best group the visible compressed representations in each view according to the k-means clustering objective. In the late fusion step of the proposed method, we encode each view’s clustering result as a zero-one matrix, of which each row serves as a compressed representation of the corresponding instance. More specifically, the proposed method performs kernel k-means clustering on the visible instances in each view and then performs a late fusion of the clustering results from different views. ![]() To this end, we propose a late fusion method for incomplete multiview clustering. However, these studies overlook the fact that clustering results with the visible instances in each view could be reliable under the random missing assumption accordingly, it seems that learning a final clustering decision via late fusion of the clustering results from incomplete views would be more natural. Most existing studies in the field of incomplete multiview clustering have focused on early fusion strategies, for example, learning subspace from multiple views. In real-world applications of multiview clustering, some views may be incomplete due to noise, sensor failure, etc. ![]()
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