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pair-wise co-regularizers

Pair-wise co-regularizers are mathematical penalty terms used in multi-view machine learning and clustering algorithms to enforce consistency among representations or hypotheses learned from different feature views of the same data. In scenarios where data points are characterized across multiple distinct sets of features, these regularizers directly measure and penalize discrepancies between the outputs, spectral embeddings, or cluster assignments derived for every pair of views during joint optimization. By minimizing these cross-view differences, pair-wise co-regularizers encourage distinct views to agree on the underlying data partition or latent structure, providing a direct view-to-view alignment mechanism that contrasts with centroid-based schemes that regularize each individual view toward a common global consensus.

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Co-regularized Multi-view Spectral Clustering

Co-regularized Multi-view Spectral Clustering

Abhishek Kumar, Piyush Rai, Hal Daumé

OrganizationsUniversity of MarylandUniversity of Utah

Why you should read this

Proposes a multi-view spectral clustering framework that co-regularizes graph Laplacians across different data representations to find consistent cluster assignments across diverse views.

In many clustering problems, we have access to multiple views of the data each of which could be individually used for clustering. Exploiting information from multiple views, one can hope to find a clustering that is more accurate than the ones obtained using the individual views. Often these different views admit same underlying clustering of the data, so we can approach this problem by looking for clusterings that are consistent across the views, i.e., corresponding data points in each view should have same cluster membership. We propose a spectral clustering framework that achieves this goal by co-regularizing the clustering hypotheses, and propose two co-regularization schemes to accomplish this. Experimental comparisons with a number of baselines on two synthetic and three real-world datasets establish the efficacy of our proposed approaches.

Added

2026-09-25