Autoencoder-Based Latent Block-Diagonal Representation for Subspace Clustering

Xu, Yesong; Chen, Shuo; Li, Jun; Han, Zongyan; Yang, Jian

doi:10.1109/tcyb.2020.3031666

Cited by 16 publications

(7 citation statements)

References 46 publications

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“…Original data: X ples may contain noise corruption, it is difficult to satisfy such a purpose in practice (Peng et al 2018;Xu et al 2020).…”

Section: Sample: XImentioning

confidence: 99%

“…1). Towards addressing the above-mentioned problem, metric-based subspace clustering methods have been proposed to obtain a "good" similarity matrix (Yang et al 2020;Wang et al 2019;Liang et al 2019). However, the self-expressive-based strategy explores the degree of linear correlation between samples to acquire the similarity matrix.…”

Section: Selfmentioning

confidence: 99%

“…In addition, to achieve better performance on the CI-FAR10 dataset, we extract features from the CIFAR10 dataset following the same setting in (Xu et al 2020). In this paper, we randomly selected 1000, 500, and 800 samples for each class from the MNIST, USPS and CIFAR10 datasets, respectively.…”

Section: Experimental Settingsmentioning

confidence: 99%

“…Compared Methods: We compare our proposed LASC algorithm against the following four spectral-based subspace clustering methods, including SSC (Elhamifar and Vidal 2013), LSR (Lu et al 2012), LRR (Liu et al 2013) and EnSC (You et al 2016). Three metric-based subspace clustering strategies are also adopted, including FGNSC (Yang et al 2020), OSC (Wang et al 2019) and autoSC (Liang et al 2019).…”

Section: Experimental Settingsmentioning

confidence: 99%

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Linearity-Aware Subspace Clustering

Chen

et al. 2022

AAAI

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Obtaining a good similarity matrix is extremely important in subspace clustering. Current state-of-the-art methods learn the similarity matrix through self-expressive strategy. However, these methods directly adopt original samples as a set of basis to represent itself linearly. It is difficult to accurately describe the linear relation between samples in the real-world applications, and thus is hard to find an ideal similarity matrix. To better represent the linear relation of samples, we present a subspace clustering model, Linearity-Aware Subspace Clustering (LASC), which can consciously learn the similarity matrix by employing a linearity-aware metric. This is a new subspace clustering method that combines metric learning and subspace clustering into a joint learning framework. In our model, we first utilize the self-expressive strategy to obtain an initial subspace structure and discover a low-dimensional representation of the original data. Subsequently, we use the proposed metric to learn an intrinsic similarity matrix with linearity-aware on the obtained subspace. Based on such a learned similarity matrix, the inter-cluster distance becomes larger than the intra-cluster distances, and thus successfully obtaining a good subspace cluster result. In addition, to enrich the similarity matrix with more consistent knowledge, we adopt a collaborative learning strategy for self-expressive subspace learning and linearity-aware subspace learning. Moreover, we provide detailed mathematical analysis to show that the metric can properly characterize the linear correlation between samples.

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“…Original data: X ples may contain noise corruption, it is difficult to satisfy such a purpose in practice (Peng et al 2018;Xu et al 2020).…”

Section: Sample: XImentioning

confidence: 99%

Section: Selfmentioning

confidence: 99%

Section: Experimental Settingsmentioning

confidence: 99%

Section: Experimental Settingsmentioning

confidence: 99%

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Linearity-Aware Subspace Clustering

Chen

et al. 2022

AAAI

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“…Thus, RBDR can handle noise without prior knowledge. Xu et al [30] proposed a latent BDR (LBDR) model to perform the subspace clustering on a nonlinear structure, which jointly learns an autoencoder and a BDR matrix. The autoencoder learns features from the nonlinear samples and then uses the learned features as a new dictionary for a linear model with block-diagonal regularization to ensure good performance for spectral clustering.…”

Section: A Bdrmentioning

confidence: 99%

Active Block Diagonal Subspace Clustering

Xie

Wang

2021

IEEE Access

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Subspace clustering aims to find clusters in the low-dimensional subspaces for high-dimensional data. Subspace clustering with Block Diagonal Representation (BDR) maintains the number of connected components of the graph by Laplacian rank constraint, and the learned affinity matrix shows a block diagonal structure, which will achieve a good segmentation for the dataset by spectral clustering. However, the subspaces of real data may overlap and the learned affinity matrix may be imprecise. In this work, we propose an Active learning framework for BDR(ABDR) to acquire and incorporate prior knowledge to improve the subspace clustering performance. An active selection strategy is designed to acquire labels of the informative data points from both the skeleton of clusters and the boundaries of clusters, and then the labeled data are converted into pairwise constraints, which are incorporated into BDR. The optimization of the new objective function is given and the convergence of ABDR is discussed. Experimental results on three images datasets (MNIST, and one UCI dataset(ISOLET) demonstrate the effectiveness of ABDR on complex clustering tasks and show that ABDR is superior to multiple state-of-the-art active clustering and learning techniques.

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Learnable low-rank latent dictionary for subspace clustering

Chen

et al. 2021

Pattern Recognition

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Autoencoder-Based Latent Block-Diagonal Representation for Subspace Clustering

Cited by 16 publications

References 46 publications

Linearity-Aware Subspace Clustering

Linearity-Aware Subspace Clustering

Active Block Diagonal Subspace Clustering

Learnable low-rank latent dictionary for subspace clustering

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