  In recent years, multi-view learning technologies for various applications
have attracted a surge of interest. Due to more compatible and complementary
information from multiple views, existing multi-view methods could achieve more
promising performance than conventional single-view methods in most situations.
However, there are still no sufficient researches on the unified framework in
existing multi-view works. Meanwhile, how to efficiently integrate multi-view
information is still full of challenges. In this paper, we propose a novel
multi-view learning framework, which aims to leverage most existing graph
embedding works into a unified formula via introducing the graph consensus
term. In particular, our method explores the graph structure in each view
independently to preserve the diversity property of graph embedding methods.
Meanwhile, we choose heterogeneous graphs to construct the graph consensus term
to explore the correlations among multiple views jointly. To this end, the
diversity and complementary information among different views could be
simultaneously considered. Furthermore, the proposed framework is utilized to
implement the multi-view extension of Locality Linear Embedding, named
Multi-view Locality Linear Embedding (MvLLE), which could be efficiently solved
by applying the alternating optimization strategy. Empirical validations
conducted on six benchmark datasets can show the effectiveness of our proposed
method.

Machine Learning