  In recent years, the attention mechanism contributes significantly to
hypergraph based neural networks. However, these methods update the attention
weights with the network propagating. That is to say, this type of attention
mechanism is only suitable for deep learning-based methods while not applicable
to the traditional machine learning approaches. In this paper, we propose a
hypergraph based sparse attention mechanism to tackle this issue and embed it
into dictionary learning. More specifically, we first construct a sparse
attention hypergraph, asset attention weights to samples by employing the
$\ell_1$-norm sparse regularization to mine the high-order relationship among
sample features. Then, we introduce the hypergraph Laplacian operator to
preserve the local structure for subspace transformation in dictionary
learning. Besides, we incorporate the discriminative information into the
hypergraph as the guidance to aggregate samples. Unlike previous works, our
method updates attention weights independently, does not rely on the deep
network. We demonstrate the efficacy of our approach on four benchmark
datasets.
