Automated auscultation advances the detection of respiratory diseases, especially in areas with limited resources where traditional diagnostic methods are unavailable. On the other hand, the scarcity of auscultation datasets limits the automation performance, prompting the needs for data augmentation methods. However, most of the existing methods neglect the difference in acoustic sounds that requires personalized augmentation strategies. To address this, we propose a Progressive-Adaptive Spectral Augmentation (PASA), which is one of the first paradigms to adaptively select the best augmentation strategy for each sample. The PASA innovatively treats augmentation selection problem as a Markov Decision Process (MDP), creating an alternating loop between the diagnostic model and the augmentation selection. The agent selects the optimal augmentation operations and magnitudes via a task-specific design, including state construction, action sampling, Hybrid Batch-Sample (HBS) strategy execution, and reward guidance. The HBS strategy initially applies uniform augmentation across mini-batches while collecting sample-specific performance statistics. When model performance stabilizes, it transits to sample-level augmentation based on accumulated difficulty assessments. This two-phase design balances computational complexity with personalization. Extensive experiments across three benchmark datasets demonstrate that the PASA outperforms the state-of-the-art methods, pioneering a transformative paradigm for adaptive data augmentation in automated auscultation.

Graph-based incomplete multi-view clustering algorithms have gathered much attention due to their impressive clustering performance. However, existing methods primarily leverage intra-view correlation from observed views, while ignoring the exploration of explicit compensation relationships between different views. Moreover, these methods need post-processing to get labels, and the separate steps lack negotiation, which may lead to sub-optimal solutions. To address these issues, we propose a Cross-view Anchor Graph Learning and Factorization (AGLF) method. AGLF develops an Anchor Graph Completion (AGC) framework that explicitly learn the missing subgraph structures. Instead of requiring post-processing, AGC directly produces soft labels. By establishing a third-order tensor of soft labels, it employs the tensor Schatten p-norm to enhance anchor graph learning and factorization. To significantly improve the quality of subgraph learning, AGLF incorporates compensation subgraphs from supplementary views into the AGC framework, enabling the construction of a better anchor graph for label learning. An optimization algorithm is devised to solve the objective function. Experimental results across various datasets demonstrate the effectiveness of our method.

Cross-scene hyperspectral image (HSI) recognition aims to assign a unique label to each pixel in the target scene by transferring knowledge from the source scene. Existing methods primarily rely on fully labeled source data and either partially labeled or unlabeled target data. No prior work has addressed the more challenging scenario of cross-scene recognition without label guidance in both scenes. To bridge this gap, we present the first study on cross-scene HSI clustering, proposing an anchor-guided discriminative subspace alignment and clustering (ADSAC) framework that follows a well-structured three-step learning paradigm to effectively mitigate distribution shifts. Specifically, we first develop an anchor-promoted graph learning (APGL) model to efficiently derive accurate clustering labels for the source scene by leveraging anchor-based structural information. Next, we propose a discriminative cross-scene subspace alignment (DCSA) model to improve feature discriminability and reduce distribution discrepancies. Finally, labels of the target scene are inferred after source clustering and cross-scene alignment. To solve the formulated models, we design tailored optimization algorithms to ensure high-quality learning. Extensive experiments demonstrate the superiority of the proposed framework over state-of-the-art methods.

Multi-view clustering of remote sensing data presents significant challenges, as it integrates diverse data representations to improve Earth observation. Although existing anchor graph-based methods have yielded promising results, they generally exhibit two key limitations: (1) the time-consuming process of directly exploring pixel clustering structures, and (2) insufficient modeling of high-order correlations among different views. To address these issues, we propose an Efficient Tensorized multi-view anchor graph clustering method with Affinity Propagation (ETAP) for remote sensing data. Based on superpixel preprocessing, anchor graphs are learned from view-specific pixels and anchors, while compressed anchor graphs are simultaneously learned from the view-specific anchors. An adaptive weighting scheme is introduced to facilitate the learning of these anchor graphs. To capture high-order correlations, tensor Schatten p-norm regularization is applied to the compressed anchor graphs. A connectivity constraint is introduced to uncover the clustering structures of anchors. Finally, pixel clustering structures are then efficiently revealed from the pseudo-labeled anchors through affinity propagation without requiring additional clustering steps. To solve the proposed formulation, we develop an alternating optimization algorithm. Extensive experiments on three public datasets demonstrate the efficacy and efficiency of the proposed method over state-of-the-art methods.

Incomplete multi-view clustering presents significant challenges due to missing views. Although many existing graph-based methods aim to recover missing instances or complete similarity matrices with promising results, they still face several limitations: (1) Recovered data may be unsuitable for spectral clustering, as these methods often ignore guidance from spectral analysis; (2) Complex optimization processes require high computational burden, hindering scalability to large-scale problems; (3) Most methods do not address the rotational mismatch problem in spectral embeddings. To address these issues, we propose a highly efficient rotation-invariant spectral embedding (RISE) method for scalable incomplete multi-view clustering. RISE learns view-specific embeddings from incomplete bipartite graphs to capture the complementary information. Meanwhile, a complete consensus representation with second-order rotation-invariant property is recovered from these incomplete embeddings in a unified model. Moreover, we design a fast alternating optimization algorithm with linear complexity and promising convergence to solve the proposed formulation. Extensive experiments on multiple datasets demonstrate the effectiveness, scalability, and efficiency of RISE compared to the state-of-the-art methods.

Anchor-based incomplete multi-view clustering methods utilize anchors to uncover clustering structures. However, relying on anchor graphs for producing final indicators is indirect, which can lead to information loss and suboptimal outcomes. Besides, most methods neglect the potential of anchors for imputing missing views. To address these limitations, we propose a Joint View Imputation and Label Generation (JVILG) method. JVILG comprises the Anchor-based tensorized Label Generation (ALG) module for generating clustering labels and the Anchor-based sparse regularized Subspace Correlation (ASC) module for recovering missing views. The ALG module explicitly connects data observations, the fine-grained anchor matrix, and soft label matrices within a reconstruction framework through a membership matrix, while imposing tensor Schatten p-norm regularization on the constructed label tensor to capture spatial correlations among views. Meanwhile, the ASC module directly uses fine-grained anchors to impute missing data in respective views. By integrating the ALG and ASC modules, JVILG enhances synergy between different tasks and mitigates the impact of missing information on clustering. Experimental results on six datasets demonstrate the effectiveness of JVILG compared to both shallow and deep state-of-the art methods.The code is available at https://github.com/W-Xinxin/JVILG.

Pansharpening fuses lower-resolution multispectral (LRMS) images with high-resolution panchromatic (PAN) images to generate high-resolution multispectral (HRMS) images that preserves both spatial and spectral information. Most deep pansharpening methods face challenges in cross-modal feature extraction and fusion, as well as in exploring the similarities between the fused image and both PAN and LRMS images. In this paper, we propose a spatial-spectral similarity-guided fusion network (S3FNet) for pansharpening. This architecture is composed of three parts. Specifically, a shallow feature extraction layer learns initial spatial, spectral and fused features from PAN and LRMS images. Then, a multi-branch asymmetric encoder, consisting of spatial, spectral and fusion branches, generates corresponding high-level features at different scales. A multi-scale reconstruction decoder, equipped with a well-designed cross-feature multi-head attention fusion block, processes the intermediate feature maps to generate HRMS images. To ensure HRMS images retain maximum spatial and spectral information, a similarity-constrained loss is defined for network training. Extensive experiments demonstrate the effectiveness of our S3FNet over state-of-the-art methods. The code is released at https://github.com/ZhangYongshan/S3FNet.

Hyperspectral image (HSI) clustering has attracted increasing attention in recent years, because it doesn’t rely on labeled pixels. However, it is a challenging task due to the complex spectral-spatial structure. The emergence of large-scale HSIs introduces a new challenge in terms of heightened computational complexity. To address the above challenges, in this paper, we propose a structured anchor projected clustering (SAPC) model for large-scale HSIs. Specifically, we exploit spatial information reflecting in the generated superpixels to perform denoising and generate anchors. Based on the preprocessing, we simultaneously learn a pixel-anchor graph and an anchor-anchor graph in a projected feature space. Meanwhile, the rank-constraint is imposed on the Laplacian matrix related to the anchor-anchor graph. To uncover the clustering structure, we design a clustering inference strategy to propagate clustering labels from anchors to pixels based on the dual graphs. Additionally, we propose an efficient optimization strategy for the formulated SAPC model with linear time complexity in terms of the number of pixels. Since the anchor-anchor graph is with much smaller size, it is high efficient to obtain the structured anchors with pseudo labels. Thus, the clustering process is significantly accelerated. Extensive experiments on multiple large-scale HSI datasets demonstrates the superiority of our SAPC over the state-of-the-art methods. The source code is released athttps://github.com/ZhangYongshan/SAPC.

Multiview clustering task groups objects using multiple properties, such as RGB images, infrared images, and texture information. However, incomplete multi-view clustering faces significant challenges due to missing views that hinder clustering performance. This paper proposes a Discriminative Feature Recovery and Tensorized Matrix Factorization method (DFRTMF) that effectively recovers missing views, learns low-dimensional discriminative embeddings, and enables direct clustering. DFRTMF addresses high dimensionality through projection learning and enables the output of soft indicators. To improve projection and facilitate the recovery of missing views, we propose an uncorrelated constraint based on the scatter matrix of the recovered complete data, exploring the correlations between observed and missing views. To capture high-order correlations among views, a low-rank tensor constraint based on tensor Schatten p-norm regularization is applied to a third-order tensor composed of soft indicator matrices. DFRTMF adaptively controls the inter-coordination between these factorizations using view weights to optimally explore complementary information. Furthermore, we propose an alternating optimization algorithm based on the Alternating Direction Method of Multipliers to effectively solve the proposed objective function. Extensive experiments across diverse datasets demonstrate the effectiveness of DFRTMF compared to the state-of-the-art methods.

Hyperspectral image (HSI) clustering is challenging to partition pixels into different clusters due to the complex spatial distribution and high-correlated spectrum. Subspace clustering is a representative learning paradigm and has shown competitive performance in HSIs. Most existing methods ignore potential spatial or structural information and show difficulties in dealing with large-scale HSIs. In this paper, we propose an elastic graph fusion subspace clustering (EGFSC) framework that can flexibly incorporate spectral, spatial and structural information for large HSI clustering. Instead of performing pixel-level learning, superpixel-level learning is conducted according to the generated superpixels to lessen computation burden and memory cost. To explore structural information in two perspectives, a superpixel graph and a band graph are constructed based on the superpixel features. Considering the incompatible sizes of the two graphs, we present three effective dual graph fusion strategies to fuse them in different ways. With these graph fusion strategies, EGFSC is able to improve clustering performance by simultaneously considering spatial and structural information. To solve the proposed framework, we present a closed-form solution for easy implementation. Experiments demonstrate that the proposed EGFSC obtains 70.08%, 75.76%, 87.28% and 77.23% clustering accuracies on the four HSI datasets and outperforms the state-of-the-art methods. The source code is released athttps://github.com/ZhangYongshan/EGFSC.

Existing multiview clustering methods have achieved remarkable success for general images (GIs), but still have many limitations for clustering multimodal remote sensing images (RSIs). For example, these methods are sensitive to noise and spectral variability, ignore the diverse spatial structure information across modalities, or are computationally prohibitive for large-scale RSIs, thereby limiting their applications. This article proposes a multiscale spectral-spatial anchor graph fusion (MSSAGF) method for multimodal RSI clustering. MSSAGF develops a superpixel-based nonlinear neighborhood recovery strategy to reduce noise while enhancing spatial smoothness in multimodal RSIs. Using spatial-aware anchors to extract local spatial information for each modality, MSSAGF introduces multiscale local spectral-spatial anchor graphs to capture nonlinear correlations between the pixels and their corresponding local regions. A small number of anchors effectively reduces graph construction and partitioning costs, making the time complexity of MSSAGF nearly linear. This ensures that it is computationally feasible for large-scale RSIs. Finally, MSSAGF develops an adaptive fusion mechanism to fuse multiscale local anchor graphs into a unified global anchor graph, integrating complementary information across multiple modalities while directly obtaining the final clustering results. The experimental results on three multimodal RSI datasets demonstrate the superiority of our proposed method over state-of-the-art methods. Our code is publicly available athttps://github.com/W-Xinxin/MSSAGF.

Graph-based multi-view clustering has attracted remarkable attention due to its impressive performance. However, the typical framework consisting of graph learning and indicator generation may fail to align learned graphs with the underlying data structure due to the unidirectional pipeline from refined graphs to indicator generation. Another common problem is the inadequate prior information in graph learning methods. This paper proposes a Bidirectional Probabilistic Multi-graph Learning and Decomposition (BPMLD) method by establishing an explicit bidirectional pipeline between graph learning and indicator generation for multi-view clustering. Specifically, we design a confidence term based on clustering probability indicators and fuse it with graph learning to form clustering confidence driven graph learning. Meanwhile, graph tensor learning is introduced to recover the high-order correlations among the refined graphs. We further propose a multi-graph probability decomposition module to adaptively produce cluster indicators with probability representation from the refined graphs. The seamless integration between graph learning and indicator generation enables them to interact directly and enhance each other. To solve the proposed model, we design an effective optimization algorithm. Extensive experiments demonstrate the effectiveness of our method compared to state-of-the-art methods. The code is available at: https://github.com/W-Xinxin/BPMLD.

Anchor graph-based multiview clustering (AGMVC) demonstrates high efficiency and satisfactory performance. However, it still suffers from limitations such as single-structure similarity measurement, high time expenditure for large-scale anchor graph partitioning, and limited generalization ability. To alleviate the instability problem of single-structure information, this article proposes an anchor graph construction method that learns local and global (LG) structures simultaneously. To eliminate the need for graph partitioning and address the out-of-sample problem, we develop a landmark learning method to produce structural anchors, and further propose a pseudo-supervision affinity propagation (PSAP) framework. This framework jointly optimizes graph construction and landmark learning to disentangle the in-cluster distribution between samples and anchors while accelerating convergence. In addition, our framework introduces a clustering inference partition (CIP) strategy to directly output clustering results without the need for time-consuming postprocessing. Extensive experiments validate the efficiency and effectiveness of our framework. Our code is publicly available at https://github.com/W-Xinxin/PSAP.

Anomaly detection for hyperspectral images (HSIs) is a challenging problem to distinguish a few anomalous pixels from a majority of background pixels. Most existing methods cannot simultaneously explore both structural and spatial information from global and local perspectives. In this letter, we propose a stacked graph fusion denoising autoencoder (SGFDAE) for hyperspectral anomaly detection. Specifically, the global and local graphs are constructed from an HSI to explore potential structural and spatial information. With the designed graph fusion strategy, an advanced graph denoising autoencoder with deep architecture is developed in a hierarchical manner. To achieve better reconstruction and detection, a greedy layerwise unsupervised pretraining strategy is presented for network training. Experiments show that SGFDAE achieves 97.17%, 98.43%, and 98.90% detection accuracies by averaging the results of the datasets from three different scenes and outperforms the state-of-the-art methods.

Band selection aims at selecting a subset of representative bands from original hyperspectral images (HSIs) to alleviate data redundancy. There are at least two issues existing in previous methods. First, most of them ignore global or local structural information without considering both two aspects. Second, the high-order correlations among spectral bands are not explored during learning. In this paper, we propose a tensorial global-local graph self-representation (TGSR) method for hyperspectral band selection. Specifically, we segment the HSI into diverse superpixels to show the inherent spectral-spatial structures. Based on the generated superpixels, we learn the global and local graphs to explore complex structural information from global pixels and local regions. To alleviate the computational burden, a transformation is designed for easy graph convolution of global graph and pixel spectral matrix. With global and local knowledge, we formulate a global-local graph self-representation model to conduct band correlation learning in a self-weighted manner. To explore the high-order correlations among bands, we reorganize the self-representation coefficient matrices into a tensor with low-rank constraint. We design an alternating optimization algorithm to solve the proposed model. The most representative band is selected from each band subset by performing spectral clustering on the constructed affinity matrix. Experiments on HSI datasets verify the effectiveness of our method over the state-of-the-art methods. The source code is released athttps://github.com/ZhangYongshan/TGSR.

Hyperspectral band selection aims to identify an optimal sub-set of bands from hyperspectral images (HSIs). Most existing methods explore the relationships between pair-wise pixels in a fixed graph. However, the quality of the initial fixed graph may be influenced by noises and user-defined parameters that may not be optimal for HSI analysis. In this paper, we pro-pose a graph learning based autoencoder (GLAE) to achieve unsupervised hyperspectral band selection. Using the relationships of pair-wise pixels within HSIs, GLAE constructs the initial graph to characterize the geometric structures of HSIs and then adjusts the graph to adapt the band selection process. To solve the proposed model, we intoduce an alternative optimization algorithm. Experiments and comparisons on three HSI datasets demonstrate that the proposed GLAE achieves better results over the state-of-the-art methods.

Unsupervised band selection is an essential step in preprocessing hyperspectral images (HSIs) to select informative bands. Most existing methods exploit the spatial information from the entire HSI while ignoring the difference between diverse homogeneous regions. Moreover, traditional methods utilize the limited size of data for model training that may result in degraded generalization performance. In this article, we propose a marginalized graph self-representation (MGSR) method for unsupervised hyperspectral band selection. To explore the spatial information from diverse homogenous regions, MGSR generates the segmentations of an HSI by superpixel segmentation and records the relationships between adjacent pixels of the same segmentation in a structural graph. Meanwhile, to improve the generalization and robustness, infinite corrupted samples are obtained from the original pixels by introducing noises in spectral bands for model training. To solve the proposed formulation, we design an alternating optimization algorithm to marginalize out the corruption and search for the optimal solution. Experimental studies on HSI datasets demonstrate the effectiveness of the proposed MGSR and the superiority over the state-of-the-art methods. The source code is available athttps://github.com/ZhangYongshan/MGSR.

Unsupervised band selection aims to select informative spectral bands to preprocess hyperspectral images (HSIs) without using labels. Traditional band selection methods only work well on Euclidean data, but ignore structural information of pixels and spectral bands. Moreover, they treat each HSI as a whole to exploit latent spatial information while ignoring the difference of spatial distribution between diverse homogeneous regions. In this paper, we propose a robust dual graph self-representation (RDGSR) method for unsupervised band selection. RDGSR uses superpixel segmentation technique to generate homogenous regions of each HSI to extract spatial information. Based on the segmentation result, the superpixel-based similarity graph and band-based similarity graph are constructed from HSIs to record spatial and structural information. With this knowledge, the dual graph convolution is developed and thel2,1-norm is introduced in the loss function and regularization term to eliminate the noise in rows for robust and effective band selection. The novelty of RDGSR is the joint utilization of the geometric structure of pixels with spatial consistency and the geometric structure of spectral bands to enhance the performance of band selection in a robustl2,1-norm manner. An iterative optimization algorithm is designed to solve the proposed formulation. Substantial experiments on HSI datasets are conducted to verify the superiority of the proposed RDGSR over the state-of-the-art methods. The source code is available at https://github.com/ZhangYongshan/RDGSR.

In image analysis, image samples from multiple sources may contain noisy features. Due to the difficulty of obtaining label information and complex intrinsic structures, performing unsupervised feature selection on multi-view data is a challenging problem. Most existing unsupervised multi-view feature selection methods may explore only the inter-view correlations at the view-level, and ignore the explicit correlations between features across multiple views. In this paper, we propose a tensor-based unsupervised multi-view feature selection (TUFS) method. Specifically, TUFS efficiently explores the full-order interactions among multi-view data without physically building a tensor. Besides, multiple local geometric structures for different views are constructed to facilitate unsupervised feature selection. To solve the proposed model, we design an alternating optimization algorithm. Experiments and comparisons on three image datasets demonstrate that the proposed TUFS yields better performance over the state-of-the-art methods.

Extreme Learning Machine (ELM) is an elegant technique for training Single-hidden Layer Feedforward Networks (SLFNs) with extremely fast speed that attracts significant interest recently. One potential weakness of ELM is the random generation of the input weights and hidden biases, which may deteriorate the classification accuracy. In this paper, we propose a new Memetic Algorithm (MA) based Extreme Learning Machine (M-ELM) for classification problems. M-ELM uses Memetic Algorithm which is a combination of population-based global optimization technique and individual-based local heuristic search method to find optimal network parameters for ELM. The optimized network parameters will enhance the classification accuracy and generalization performance of ELM. Experiments and comparisons on 22 benchmark data sets demonstrate that M-ELM is able to provide highly competitive results compared with other state-of-the-art varieties of ELM algorithms.