  With the development of multimedia era, multi-view data is generated in
various fields. Contrast with those single-view data, multi-view data brings
more useful information and should be carefully excavated. Therefore, it is
essential to fully exploit the complementary information embedded in multiple
views to enhance the performances of many tasks. Especially for those
high-dimensional data, how to develop a multi-view dimension reduction
algorithm to obtain the low-dimensional representations is of vital importance
but chanllenging. In this paper, we propose a novel multi-view dimensional
reduction algorithm named Auto-weighted Mutli-view Sparse Reconstructive
Embedding (AMSRE) to deal with this problem. AMSRE fully exploits the sparse
reconstructive correlations between features from multiple views. Furthermore,
it is equipped with an auto-weighted technique to treat multiple views
discriminatively according to their contributions. Various experiments have
verified the excellent performances of the proposed AMSRE.
