Self-supervised locality preserving low-pass graph convolutional embedding for large-scale hyperspectral image clustering
Y Ding, Z Zhang, X Zhao, Y Cai, S Li… - IEEE Transactions on …, 2022 - ieeexplore.ieee.org
Due to prior knowledge deficiency, large spectral variability, and high dimension of
hyperspectral image (HSI), HSI clustering is extremally a fundamental but challenging task …
hyperspectral image (HSI), HSI clustering is extremally a fundamental but challenging task …
Generalized nonconvex low-rank tensor approximation for multi-view subspace clustering
The low-rank tensor representation (LRTR) has become an emerging research direction to
boost the multi-view clustering performance. This is because LRTR utilizes not only the …
boost the multi-view clustering performance. This is because LRTR utilizes not only the …
Unsupervised self-correlated learning smoothy enhanced locality preserving graph convolution embedding clustering for hyperspectral images
Y Ding, Z Zhang, X Zhao, W Cai, N Yang… - … on Geoscience and …, 2022 - ieeexplore.ieee.org
Hyperspectral image (HSI) clustering is an extremely fundamental but challenging task with
no labeled samples. Deep clustering methods have attracted increasing attention and have …
no labeled samples. Deep clustering methods have attracted increasing attention and have …
Beyond linear subspace clustering: A comparative study of nonlinear manifold clustering algorithms
M Abdolali, N Gillis - Computer Science Review, 2021 - Elsevier
Subspace clustering is an important unsupervised clustering approach. It is based on the
assumption that the high-dimensional data points are approximately distributed around …
assumption that the high-dimensional data points are approximately distributed around …
Learning a self-expressive network for subspace clustering
State-of-the-art subspace clustering methods are based on the self-expressive model, which
represents each data point as a linear combination of other data points. However, such …
represents each data point as a linear combination of other data points. However, such …
Adaptive transition probability matrix learning for multiview spectral clustering
Multiview clustering as an important unsupervised method has been gathering a great deal
of attention. However, most multiview clustering methods exploit the self-representation …
of attention. However, most multiview clustering methods exploit the self-representation …
Superpixel contracted neighborhood contrastive subspace clustering network for hyperspectral images
Deep subspace clustering (DSC) has achieved remarkable performances in the
unsupervised classification of hyperspectral images. However, previous models based on …
unsupervised classification of hyperspectral images. However, previous models based on …
Enforced block diagonal subspace clustering with closed form solution
Subspace clustering aims to fit each category of data points by learning an underlying
subspace and then conduct clustering according to the learned subspace. Ideally, the …
subspace and then conduct clustering according to the learned subspace. Ideally, the …
Rank consistency induced multiview subspace clustering via low-rank matrix factorization
Multiview subspace clustering has been demonstrated to achieve excellent performance in
practice by exploiting multiview complementary information. One of the strategies used in …
practice by exploiting multiview complementary information. One of the strategies used in …
Sparse subspace clustering with entropy-norm
In this paper, we provide an explicit theoretical connection between Sparse subspace
clustering (SSC) and spectral clustering (SC) from the perspective of learning a data …
clustering (SSC) and spectral clustering (SC) from the perspective of learning a data …