From Spectrum-free towards Baseline-view-free: Double-track Proximity Driven Multi-view Clustering

Current multi-view clustering (MVC) techniques generally focus only on the relationship between anchors and samples, while overlooking that between anchors. Moreover, due to the lack of data labels, the cluster order is inconsistent across views and accordingly anchors encounter misalignment, which will confuse the graph structure and disorganize cluster representation. Even worse, it typically brings variance during forming spectral embedding, degenerating the stability of clustering results. In response to these concerns, in the paper we propose a MVC approach named DTP-SF-BVF. Concretely, we explicitly exploit the geometric properties between anchors via self-expression learning skill, and utilize topology learning strategy to feed captured anchor-anchor features into anchor-sample graph so as to explore the manifold structure hidden within samples more adequately. To reduce the misalignment risk, we introduce a permutation mechanism for each view to jointly rearrange anchors according to respective view characteristics. Besides not involving selecting the baseline view, it also can coordinate with anchors in the unified framework and thereby facilitate the learning of anchors. Further, rather than forming spectrum and then performing embedding partitioning, based on the criterion that samples and clusters should be hard assignment, we manage to construct the cluster labels directly from original samples using the binary strategy, not only preserving the data diversity but avoiding variance. Experiments on multiple publicly available datasets confirm the effectiveness of proposed DTP-SF-BVF method.

Current multi-view clustering (MVC) methods analyze how data points relate to reference points ("anchors") but ignore connections between anchors themselves. Without labels, anchors may misalign across views, disrupting cluster structure and reducing result stability.To address this, we propose DTP-SF-BVF, a new MVC approach that:1. Models anchor relationships using geometric learning to better capture hidden data patterns.2. Aligns anchors across views via an adaptive permutation mechanism, avoiding manual reference selection. 3. Directly generates cluster labels from raw data (instead of spectral methods), preserving diversity while minimizing instability.Tests on real-world datasets show our method improves clustering accuracy and robustness. This work advances unsupervised learning by refining how multi-view data is structured and aligned.