2022
DOI: 10.1109/access.2022.3200482
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Multi-Scale Deep Subspace Clustering With Discriminative Learning

Abstract: Deep subspace clustering methods have achieved impressive clustering performance compared with other clustering algorithms. However, most existing methods suffer from the following problems: 1) they only consider the global features but neglect the local features in subspace self-expressiveness learning; 2) they neglect the discriminative information of each self-expressiveness coefficient matrix; 3) they ignore the useful long-range dependencies and positional information in feature representation learning. T… Show more

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Cited by 9 publications
(2 citation statements)
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“…We compared DSGCC with ten clustering baselines, including K-means [15], PCA [16], SC [17], SuperPCA [25], DEC [26], AE+K-means, DSC [32], DS 3 C-Net [29], DGAE [34], and MDSCDL [33]. Especially, K-means, PCA, SC, and SuperPCA are traditional shallow approaches.…”
Section: Baselines and Comparative Methodsmentioning
confidence: 99%
See 1 more Smart Citation
“…We compared DSGCC with ten clustering baselines, including K-means [15], PCA [16], SC [17], SuperPCA [25], DEC [26], AE+K-means, DSC [32], DS 3 C-Net [29], DGAE [34], and MDSCDL [33]. Especially, K-means, PCA, SC, and SuperPCA are traditional shallow approaches.…”
Section: Baselines and Comparative Methodsmentioning
confidence: 99%
“…Because of the success of the autoencoder in computer vision, a lot of autoencoder-based approaches are presented for hidden representation learning. Wang et al [33] proposed a multi-scale deep subspace clustering with discriminative learning (MDSCDL) by learning the multiscale self-expressiveness matrices. Zhang et al [34] presented the dual graph autoencoder (DGAE) to capture information for HSI, fully exploiting both geometric characteristics of pixels with spatial-spectral information to enhance high-level information extraction.…”
mentioning
confidence: 99%