Efficient Anchor Learning-based Multi-view Clustering -- A Late Fusion Method

Abstract

Anchor enhanced multi-view late fusion clustering has attracted numerous researchers' attention for its high clustering accuracy and promising efficiency. However, in the existing methods, the anchor points are usually generated through sampling or linearly combining the samples within the datasets, which could result in enormous time consumption and limited representation capability. To solve the problem, in our method, we learn the view-specific anchor points by learning them directly. Specifically, in our method, we first reconstruct the partition matrix of each view through multiplying a view-specific anchor matrix by a consensus reconstruction matrix. Then, by maximizing the weighted alignment between the base partition matrix and its estimated version in each view, we learn the optimal anchor points for each view. In particular, unlike previous late fusion algorithms, which define anchor points as linear combinations of existing samples, we define anchor points as a series of orthogonal vectors that are directly learned through optimization, which expands the learning space of the anchor points. Moreover, based on the above design, the resultant algorithm has only linear complexity and no hyper-parameter. Experiments on $12$ benchmark kernel datasets and 5 large-scale datasets illustrate that the proposed Efficient Anchor Learning-based Multi-view Clustering (AL-MVC) algorithm achieves the state-of-the-art performance in both clustering performance and efficiency.