Collaborative Discriminative Manifold Embedding for Hyperspectral Imagery

Liu, Meng; Hou, Qiuling; Deng, Ning; Jing, Ling

doi:10.1109/lgrs.2017.2660518

Cited by 17 publications

(6 citation statements)

References 14 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…We evaluate the performance of the proposed IMR model visually and quantitatively in comparison with eight state-of-the-art baselines, including. Non-HDR : original spectral features (OSF); Supervised HDR : feature space discriminant analysis (FSDA) (Imani and Ghassemian, 2015), joint learning (JL) (Hong et al, 2019b); Semi-supervised subspace learning for HDR : semi-supervised local discriminant analysis (SELD) (Liao et al, 2013), collaborative discriminative manifold embedding (CDME) (Lv et al, 2017); GLP-based semi-supervised HDR : soft-label LDA (SL-LDA) (Zhao et al, 2014), semi-super- vised fisher local discriminant analysis (SSFLDA) (Wu and Prasad, 2018). …”

Section: Methodsmentioning

confidence: 99%

See 1 more Smart Citation

Learning to propagate labels on graphs: An iterative multitask regression framework for semi-supervised hyperspectral dimensionality reduction

Hong

Yokoya

Chanussot

et al. 2019

ISPRS Journal of Photogrammetry and Remote Sensing

139

View full text Add to dashboard Cite

Hyperspectral dimensionality reduction (HDR), an important preprocessing step prior to high-level data analysis, has been garnering growing attention in the remote sensing community. Although a variety of methods, both unsupervised and supervised models, have been proposed for this task, yet the discriminative ability in feature representation still remains limited due to the lack of a powerful tool that effectively exploits the labeled and unlabeled data in the HDR process. A semi-supervised HDR approach, called iterative multitask regression (IMR), is proposed in this paper to address this need. IMR aims at learning a low-dimensional subspace by jointly considering the labeled and unlabeled data, and also bridging the learned subspace with two regression tasks: labels and pseudo-labels initialized by a given classifier. More significantly, IMR dynamically propagates the labels on a learnable graph and progressively refines pseudo-labels, yielding a well-conditioned feedback system. Experiments conducted on three widely-used hyperspectral image datasets demonstrate that the dimension-reduced features learned by the proposed IMR framework with respect to classification or recognition accuracy are superior to those of related state-of-the-art HDR approaches.

show abstract

Section: Methodsmentioning

confidence: 99%

“…Semi-supervised subspace learning for HDR : semi-supervised local discriminant analysis (SELD) (Liao et al, 2013), collaborative discriminative manifold embedding (CDME) (Lv et al, 2017);…”

Section: Methodsmentioning

confidence: 99%

Learning to propagate labels on graphs: An iterative multitask regression framework for semi-supervised hyperspectral dimensionality reduction

Hong

Yokoya

Chanussot

et al. 2019

ISPRS Journal of Photogrammetry and Remote Sensing

139

View full text Add to dashboard Cite

show abstract

“…We also remark that FS is applied and used in various domains including gene selection, face recognition, handwriting identification, and remote sensing [35][36][37][38] .…”

Section: Related Workmentioning

confidence: 99%

Dimensionality reduction using singular vectors

Afshar

Usefi

2021

Sci Rep

View full text Add to dashboard Cite

A common problem in machine learning and pattern recognition is the process of identifying the most relevant features, specifically in dealing with high-dimensional datasets in bioinformatics. In this paper, we propose a new feature selection method, called Singular-Vectors Feature Selection (SVFS). Let $$D= [A \mid \mathbf {b}]$$ D = [ A ∣ b ] be a labeled dataset, where $$\mathbf {b}$$ b is the class label and features (attributes) are columns of matrix A. We show that the signature matrix $$S_A=I-A^{\dagger }A$$ S A = I - A † A can be used to partition the columns of A into clusters so that columns in a cluster correlate only with the columns in the same cluster. In the first step, SVFS uses the signature matrix $$S_D$$ S D of D to find the cluster that contains $$\mathbf {b}$$ b . We reduce the size of A by discarding features in the other clusters as irrelevant features. In the next step, SVFS uses the signature matrix $$S_A$$ S A of reduced A to partition the remaining features into clusters and choose the most important features from each cluster. Even though SVFS works perfectly on synthetic datasets, comprehensive experiments on real world benchmark and genomic datasets shows that SVFS exhibits overall superior performance compared to the state-of-the-art feature selection methods in terms of accuracy, running time, and memory usage. A Python implementation of SVFS along with the datasets used in this paper are available at https://github.com/Majid1292/SVFS.

show abstract

“…Inspired by these advantages, a great number of algorithms have been proposed based on CR and SR in recent literature. In view of CR, Lv et al [33] presented a supervised dimensionality reduction method called collaborative discriminative manifold embedding (CDME) for hyperspectral imagery. In [34], graph regularized sparsity discriminant analysis (GRSDA) was proposed for face recognition, by constructing the intrinsic and penalty graphs through SR.…”

Section: Introductionmentioning

confidence: 99%

Joint Sparsity and Collaboration Preserving Projections for Rotating Electrical Machinery Fault Diagnosis

2020

IEEE Access

View full text Add to dashboard Cite

It is always a serious challenge to deal with the influence of noise for fault detection of rotating electrical machines under varying operating environments. To extract the most discriminative features for machine fault classification, a novel dimensionality reduction algorithm called joint sparsity and collaboration preserving projections (JSCPP) is proposed in this paper, based on the platform of graph embedding framework. A joint combination of sparse representation (SR) and collaborative representation (CR) is used to construct the intrinsic and penalty graphs. SR emphasizes only on the dictionary atoms highly correlated to the query sample for local optimum while CR takes every atom into consideration for global optimum, thus applying a joint combination would make it benefit from the merits of both for graph construction. After JSCPP seeks the optimal projection directions by minimizing the intra-class compactness and maximizing the inter-class separability, a nearest neighbor classifier is then used. Through two sets of vibration data with white Gaussian noise interfered, the capability of JSCPP is demonstrated by effectively classifying different defect types and severity degrees of faulty bearings. The experimental results have also proved that the classification performance of the proposed method is much superior to the other compared algorithms in both accuracy rates and reliability.

show abstract

Collaborative Discriminative Manifold Embedding for Hyperspectral Imagery

Cited by 17 publications

References 14 publications

Learning to propagate labels on graphs: An iterative multitask regression framework for semi-supervised hyperspectral dimensionality reduction

Learning to propagate labels on graphs: An iterative multitask regression framework for semi-supervised hyperspectral dimensionality reduction

Dimensionality reduction using singular vectors

Joint Sparsity and Collaboration Preserving Projections for Rotating Electrical Machinery Fault Diagnosis

Contact Info

Product

Resources

About