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One-pass View-unaligned Clustering
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AI
DOI：10.1109/TMM.2024.3397038.png)
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Abstract
En 中文
Given a set of multi-view instances, the prevailing assumption in most existing clustering approaches is that they are complete and exhibit cross-view alignment. However, this assumption is often unrealistic. In such scenarios, it could be satisfied at the cost of data pre-processing, but this would be complex and inconsistent with practical applications. Therefore, developing more effective solutions for the View-unaligned Problem (VuP) is highly desirable. Several pioneering works have tackled the partially VuP, yet handling fully VuP remains a challenge due to the reliance on partially pre-aligned instances. In this paper, we propose One-pass View-unaligned Clustering (OpVuC) that simultaneously aligns and clusters instances in a unified framework. Specifically, we align shuffled instances with a selected template using an innovative global-local alignment scheme based on the notion of geometric invariance and separate the fully aligned instances using a relaxed $k$-means algorithm. The proposed OpVuC method can handle VuP at any alignment level without requiring any pre-aligned instances. Extensive experiments conducted on several benchmark datasets demonstrate the effectiveness and merits of the proposed OpVuC method.
Keywords:
Clustering methods
Clustering algorithms
Vectors
Scalability
Noise robustness
Task analysis
Speech recognition
Multi-view
clustering
view-unaligned problem
global-local alignment
relaxed k-means
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