One for all: A novel Dual-space Co-training baseline for Large-scale
  Multi-View Clustering
Zisen Kong Zhiqiang Fu Dongxia Chang Yiming Wang Yao Zhao
In this paper, we propose a novel multi-view clustering model, named
Dual-space Co-training Large-scale Multi-view Clustering (DSCMC). The main
objective of our approach is to enhance the clustering performance by
leveraging co-training in two distinct spaces. In the original space, we learn
a projection matrix to obtain latent consistent anchor graphs from different
views. This process involves capturing the inherent relationships and
structures between data points within each view. Concurrently, we employ a
feature transformation matrix to map samples from various views to a shared
latent space. This transformation facilitates the alignment of information from
multiple views, enabling a comprehensive understanding of the underlying data
distribution. We jointly optimize the construction of the latent consistent
anchor graph and the feature transformation to generate a discriminative anchor
graph. This anchor graph effectively captures the essential characteristics of
the multi-view data and serves as a reliable basis for subsequent clustering
analysis. Moreover, the element-wise method is proposed to avoid the impact of
diverse information between different views. Our algorithm has an approximate
linear computational complexity, which guarantees its successful application on
large-scale datasets. Through experimental validation, we demonstrate that our
method significantly reduces computational complexity while yielding superior
clustering performance compared to existing approaches.