  Multi-view clustering can explore common semantics from multiple views and
has attracted increasing attention. However, existing works punish multiple
objectives in the same feature space, where they ignore the conflict between
learning consistent common semantics and reconstructing inconsistent
view-private information. In this paper, we propose a new framework of
multi-level feature learning for contrastive multi-view clustering to address
the aforementioned issue. Our method learns different levels of features from
the raw features, including low-level features, high-level features, and
semantic labels/features in a fusion-free manner, so that it can effectively
achieve the reconstruction objective and the consistency objectives in
different feature spaces. Specifically, the reconstruction objective is
conducted on the low-level features. Two consistency objectives based on
contrastive learning are conducted on the high-level features and the semantic
labels, respectively. They make the high-level features effectively explore the
common semantics and the semantic labels achieve the multi-view clustering. As
a result, the proposed framework can reduce the adverse influence of
view-private information. Extensive experiments on public datasets demonstrate
that our method achieves state-of-the-art clustering effectiveness.
