267 research outputs found
Multi-View Clustering via Semi-non-negative Tensor Factorization
Multi-view clustering (MVC) based on non-negative matrix factorization (NMF)
and its variants have received a huge amount of attention in recent years due
to their advantages in clustering interpretability. However, existing NMF-based
multi-view clustering methods perform NMF on each view data respectively and
ignore the impact of between-view. Thus, they can't well exploit the
within-view spatial structure and between-view complementary information. To
resolve this issue, we present semi-non-negative tensor factorization
(Semi-NTF) and develop a novel multi-view clustering based on Semi-NTF with
one-side orthogonal constraint. Our model directly performs Semi-NTF on the
3rd-order tensor which is composed of anchor graphs of views. Thus, our model
directly considers the between-view relationship. Moreover, we use the tensor
Schatten p-norm regularization as a rank approximation of the 3rd-order tensor
which characterizes the cluster structure of multi-view data and exploits the
between-view complementary information. In addition, we provide an optimization
algorithm for the proposed method and prove mathematically that the algorithm
always converges to the stationary KKT point. Extensive experiments on various
benchmark datasets indicate that our proposed method is able to achieve
satisfactory clustering performance
SAWU-Net: Spatial Attention Weighted Unmixing Network for Hyperspectral Images
Hyperspectral unmixing is a critical yet challenging task in hyperspectral
image interpretation. Recently, great efforts have been made to solve the
hyperspectral unmixing task via deep autoencoders. However, existing networks
mainly focus on extracting spectral features from mixed pixels, and the
employment of spatial feature prior knowledge is still insufficient. To this
end, we put forward a spatial attention weighted unmixing network, dubbed as
SAWU-Net, which learns a spatial attention network and a weighted unmixing
network in an end-to-end manner for better spatial feature exploitation. In
particular, we design a spatial attention module, which consists of a pixel
attention block and a window attention block to efficiently model pixel-based
spectral information and patch-based spatial information, respectively. While
in the weighted unmixing framework, the central pixel abundance is dynamically
weighted by the coarse-grained abundances of surrounding pixels. In addition,
SAWU-Net generates dynamically adaptive spatial weights through the spatial
attention mechanism, so as to dynamically integrate surrounding pixels more
effectively. Experimental results on real and synthetic datasets demonstrate
the better accuracy and superiority of SAWU-Net, which reflects the
effectiveness of the proposed spatial attention mechanism.Comment: IEEE GRSL 202
- …