Semi-Supervised Subspace Clustering via Non-Negative Low-Rank Representation for Hyperspectral Images

Yang, Jian; Zhang, Dexiang; Li, Teng; Wang, Yan; Yan, Qing

doi:10.1109/rcar.2018.8621800

Cited by 4 publications

(5 citation statements)

References 10 publications

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“…However, sometimes a few labelled data points might be accessible, which can provide helpful supervised information to guide clustering algorithms to better learn the cluster structure of data. By incorporating supervised information, semi-supervised clustering methods are developed in [54,55,57]. The idea of [54,55] focuses on the refinement of coefficients matrix in self-representation models with supervised information for a more block-diagonal similarity matrix.…”

Section: Object-based Clustering Methodsmentioning

confidence: 99%

“…Different from [54,55], the authors of [56,57] propagate the label information in a graph that is obtained by solving a self-representation model. Let X l ∈ R B×l be the labelled data, X u be the unlabelled data, X = [X l , X u ], Y l ∈ R c×l be the one-hot label matrix of X l and F = [F l , F u ] be the predicted label matrix of X.…”

Section: Object-based Clustering Methodsmentioning

confidence: 99%

“…Let X l ∈ R B×l be the labelled data, X u be the unlabelled data, X = [X l , X u ], Y l ∈ R c×l be the one-hot label matrix of X l and F = [F l , F u ] be the predicted label matrix of X. The objective function of the semi-supervised clustering model, non-negative LRR (NNLRR), in [56,57] is formulated as follows:…”

Section: Object-based Clustering Methodsmentioning

confidence: 99%

“…Semi-supervised CPPSSC [54], JSSC-L [55], NNLRR [56,57] Supervised information is incorporated with a few labelled data.…”

Section: Spatial-spectral Basedmentioning

confidence: 99%

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From Model-Based Optimization Algorithms to Deep Learning Models for Clustering Hyperspectral Images

2023

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Hyperspectral images (HSIs), captured by different Earth observation airborne and space-borne systems, provide rich spectral information in hundreds of bands, enabling far better discrimination between ground materials that are often indistinguishable in visible and multi-spectral images. Clustering of HSIs, which aims to unveil class patterns in an unsupervised way, is highly important in the interpretation of HSI, especially when labelled data are not available. A number of HSI clustering methods have been proposed. Among them, model-based optimization algorithms, which learn the cluster structure of data by solving convex/non-convex optimization problems, have achieved the current state-of-the-art performance. Recent works extend the model-based algorithms to deep versions with deep neural networks, obtaining huge breakthroughs in clustering performance. However, a systematic survey on the topic is absent. This article provides a comprehensive overview of clustering methods of HSI and tracked the latest techniques and breakthroughs in the domain, including the traditional model-based optimization algorithms and the emerging deep learning based clustering methods. With a new taxonomy, we elaborated on the main ideas, technical details, advantages, and disadvantages of different types of clustering methods of HSIs. We provided a systematic performance comparison between different clustering methods by conducting extensive experiments on real HSIs. Unsolved problems and future research trends in the domain are pointed out. Moreover, we provided a toolbox that contains implementations of representative clustering algorithms to help researchers to develop their own models.

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Section: Object-based Clustering Methodsmentioning

confidence: 99%

Section: Object-based Clustering Methodsmentioning

confidence: 99%

Section: Object-based Clustering Methodsmentioning

confidence: 99%

“…Semi-supervised CPPSSC [54], JSSC-L [55], NNLRR [56,57] Supervised information is incorporated with a few labelled data.…”

Section: Spatial-spectral Basedmentioning

confidence: 99%

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From Model-Based Optimization Algorithms to Deep Learning Models for Clustering Hyperspectral Images

2023

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“…With this belief, LRR enforces the coefficient matrix to be a blockdiagonal form and thereby effectively recovers the subspace structures of the data [26]. Due to its robustness and discrimination, LRR has been intensively studied for improvement [31], [39], [41], [42] and has reaped many promising results in applications [28], [30]. As is shown in FIGURE 2, the LRR not only has robustness to noise, but also can capture the discriminative structure of the given data.…”

Section: Low-rank Representationmentioning

confidence: 99%

Integrated Sparse Coding With Graph Learning for Robust Data Representation

Zhang

Liu

2020

IEEE Access

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Sparse coding is a popular technique for achieving compact data representation and has been used in many applications. However, the instability issue often causes degeneration in practice and thus attracts a lot of studies. While the traditional graph sparse coding preserves the neighborhood structure of the data, this study integrates the low-rank representation(LRR) to fix the inconsistency of sparse coding by holding the subspace structures of the high-dimensional observations. The proposed method is dubbed low-rank graph regularized sparse coding (LogSC), which learns sparse codes and low-rank representations jointly rather than the traditional two-step approach. Since the two data representations share a dictionary matrix, the resulted sparse representation on this dictionary could be benefited from LRR. We solved the optimization problem of LogSC by using the linearized alternating direction method with adaptive penalty. Experimental results show the proposed method is discriminative in feature learning and robust to various noises. This work provides a one-step approach to integrating graph embedding in representation learning. INDEX TERMS Graph sparse coding, graph embedding, data structure preserving, robust data representation, image representation, one-step integrated graph sparse coding.

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Learning Low-Rank Representation Approximation for Few-Shot Deep Subspace Clustering

Wang,

Ye,

Wang

2024

IEEE Trans. Circuits Syst. Video Technol.

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Semi-Supervised Subspace Clustering via Non-Negative Low-Rank Representation for Hyperspectral Images

Cited by 4 publications

References 10 publications

From Model-Based Optimization Algorithms to Deep Learning Models for Clustering Hyperspectral Images

From Model-Based Optimization Algorithms to Deep Learning Models for Clustering Hyperspectral Images

Integrated Sparse Coding With Graph Learning for Robust Data Representation

Learning Low-Rank Representation Approximation for Few-Shot Deep Subspace Clustering

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