Multi-view dimensionality reduction (MvDR) is a potent approach for addressing the high-dimensional challenges in multi-view data. Recently, contrastive learning (CL) has gained considerable attention due to its superior performance. However, most CL-based methods focus on promoting consistency between any two cross views from the perspective of subspace samples, which extract features containing redundant information and fail to capture view-specific discriminative information. In this study, we propose feature- and recovery-level contrastive losses to eliminate redundant information and capture view-specific discriminative information, respectively. Based on this, we construct a novel MvDR method based on triple contrastive heads (TCH). This method combines sample-, feature-, and recovery-level contrastive losses to extract sufficient yet minimal subspace discriminative information in accordance with the information bottleneck principle. Furthermore, the relationship between TCH and mutual information is revealed, which provides the theoretical support for the outstanding performance of our method. Our experiments on five real-world datasets show that the proposed method outperforms existing methods.

This letter presents a semisupervised feature extraction based on collaborative label propagation (SSCLP) for hyperspectral images (HSIs). SSCLP first proposes a novel collaborative label propagation method to predict the labels of unlabeled data that are termed weak labels. Then, SSCLP combines the known labels and the predicted weak labels to construct two new discriminative matrices. Finally, the discriminative matrices are utilized to find an optimal transformation matrix to achieve feature extraction for HSIs. The proposed SSCLP not only preserves the compactness of intraclass and the separability of interclass but also explores the weak labels information and the local neighbor information of unlabeled data. Experiments on the Pavia University and Kennedy Space Center datasets demonstrate that the proposed SSCLP has a better performance than other related methods.

In this mymargin letter, we propose a novel supervised dimensionality reduction (DR) method termed linear discriminant analysis based on kernel-based possibilistic c-means (LDA-KPCM) for hyperspectral images (HSIs). The basic idea of this method is to use KPCM algorithm to generate different weights for different samples so that the newly-proposed method can learn the optimal transformation directions according to the relative importance of samples. The weights generated by KPCM are relatively higher for important samples but relatively lower for outliers. The experimental results on two HSI benchmark data sets demonstrate that LDA-KPCM can achieve better performance than the other state-of-the-art DR methods.

Although support vector classifiers for hyperspectral imagery traditionally exploit spectral information alone, there has been increasing interest in spatial-spectral classifiers that incorporate spatial context due to the potential for significant performance improvement over spectral-only approaches. Accordingly, a new approach for spatial-spectral classification is introduced which incorporates spatial information into a prior hyperspectral classifier driven by functional data analysis (FDA) applied to continuous spectral functions. FDA permits functional properties-such as the smoothness inherent to spectral signatures-to inform hyperspectral classification. The proposed spatial FDA (SFDA) incorporates an additional spatial coherency factor that attempts to ensure that each pixel is represented with a spectral curve that is similar to those of its nearest spatial neighbors. Experimental results demonstrate that the proposed SFDA coupled with a support vector classifier yields results superior to other state-of-the-art spatial-spectral techniques for hyperspectral classification.

Based on collaborative representation, a novel supervised dimensionality reduction method called collaborative discriminative manifold embedding (CDME) is proposed for hyperspectral imagery. In the proposed CDME, we construct both an intraclass manifold graph and an interclass manifold graph based on two structured dictionaries. In the intraclass manifold graph, the neighborhood points are selected from the dictionary with the same class. Interclass manifold graph calculates the edge weight using all points that are sampled from the dictionary with the different classes. The goal of CDME is to learn a low-dimensional feature space by preserving the intraclass reconstructive structure and the interclass geometric structure simultaneously. Finally, the 1-NN classifier is employed to verify the performance of the CDME. Experimental results demonstrate that CDME outperforms other state-of-the-art dimensionality reduction methods.

The traditional vector-based classifiers, such as support vector machine (SVM) and twin support vector machine (TSVM), cannot handle tensor data directly and may not utilize the data informations effectively. In this paper, we propose a novel classifier based on tensor data, called twin bounded support tensor machine (TBSTM) which is an extension of twin bounded support vector machine (TBSVM). Similar to TBSVM, TBSTM gets two hyperplanes and obtains the solution by solving two quadratic programming problems (QPPs). The computational complexity of each QPPs is smaller than that of support tensor machine (STM). TBSTM not only retains the advantage of TBSVM, but also has its unique superior characteristics: (1) it makes full use of the structure information of data; (2) it has acceptable or better classification accuracy compared to STM, TBSVM and SVM; (3) the computational cost is basically less than STM; (4) it can deal with large data that TBSVM is not easy to achieve, especially for small-sample-size (S3) problems; (5) it adopts alternating successive over relaxation iteration (ASOR) method to solve optimization problems which accelerates the pace of training. Finally, we demonstrate the effectiveness and superiority by the experiments based on vector and tensor data.

This letter presents a semisupervised dimension reduction method based on pairwise constraint propagation (SSDR-PCP) for hyperspectral images (HSIs). SSDR-PCP first utilizes pairwise constraint propagation, which is based on the labeled samples and k-nearest neighbor graphs to obtain more similarity information. Then SSDR-PCP applies the obtained weak supervised information of the entire training data set to construct a new similarity matrix. At last, we embed the similarity matrix to local preserving projection to achieve dimension reduction by finding the optimal transformation matrix for HSIs. The experimental results demonstrate that SSDR-PCP achieves better performance than the previous methods on two HSIs.