Multi-view low-rank dictionary learning for image classification

Wu, Fei; Jing, Xiao‐Yuan; You, Xinge; Yang, Dong; Hu, Ruimin; Yang, Jingyu

doi:10.1016/j.patcog.2015.08.012

Cited by 121 publications

(32 citation statements)

References 45 publications

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“…Each network can only capture a part of between‐group differences and identify some patients correctly, which often causes serious results, that is, certain patients cannot be identified timely to receive effective treatment. Inspired by the concept of multi‐view learning which utilizes information from multiple perspectives of an object to enhance its representation (Gong, Ke, Isard, & Lazebnik, ; Jin et al, ; Wu et al, ; Xu, Tao, & Xu, ), multiple FCNs can be used in a similar way to provide a more descriptive and informative representation on the functional organization of the brain. In the multi‐view learning, a feature set is extracted from each view to provide some different yet complementary information.…”

Section: Introductionmentioning

confidence: 99%

Enhancing the representation of functional connectivity networks by fusing multi‐view information for autism spectrum disorder diagnosis

Huang

Liu

Jin

et al. 2018

Human Brain Mapping

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Functional connectivity network provides novel insights on how distributed brain regions are functionally integrated, and its deviations from healthy brain have recently been employed to identify biomarkers for neuropsychiatric disorders. However, most of brain network analysis methods utilized features extracted only from one functional connectivity network for brain disease detection and cannot provide a comprehensive representation on the subtle disruptions of brain functional organization induced by neuropsychiatric disorders. Inspired by the principles of multi‐view learning which utilizes information from multiple views to enhance object representation, we propose a novel multiple network based framework to enhance the representation of functional connectivity networks by fusing the common and complementary information conveyed in multiple networks. Specifically, four functional connectivity networks corresponding to the four adjacent values of regularization parameter are generated via a sparse regression model with group constraint ( l2,1‐norm), to enhance the common intrinsic topological structure and limit the error rate caused by different views. To obtain a set of more meaningful and discriminative features, we propose using a modified version of weighted clustering coefficients to quantify the subtle differences of each group‐sparse network at local level. We then linearly fuse the selected features from each individual network via a multi‐kernel support vector machine for autism spectrum disorder (ASD) diagnosis. The proposed framework achieves an accuracy of 79.35%, outperforming all the compared single network methods for at least 7% improvement. Moreover, compared with other multiple network methods, our method also achieves the best performance, that is, with at least 11% improvement in accuracy.

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Section: Introductionmentioning

confidence: 99%

Enhancing the representation of functional connectivity networks by fusing multi‐view information for autism spectrum disorder diagnosis

Huang

Liu

Jin

et al. 2018

Human Brain Mapping

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“…One excellent work is the incremental K-SVD method for spatial big data representation [1,2]. Another representative work is the low-rank dictionary [3,4]. However, as we focus on the representation of continuous data sequences in the pixel domain, these methods cannot be directly applied to compress the remote sensing video data.…”

Section: Introductionmentioning

confidence: 99%

Towards Real-Time Service from Remote Sensing: Compression of Earth Observatory Video Data via Long-Term Background Referencing

Xiao

Zhu

et al. 2018

Remote Sensing

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City surveillance enables many innovative applications of smart cities. However, the real-time utilization of remotely sensed surveillance data via unmanned aerial vehicles (UAVs) or video satellites is hindered by the considerable gap between the high data collection rate and the limited transmission bandwidth. High efficiency compression of the data is in high demand. Long-term background redundancy (LBR) (in contrast to local spatial/temporal redundancies in a single video clip) is a new form of redundancy common in Earth observatory video data (EOVD). LBR is induced by the repetition of static landscapes across multiple video clips and becomes significant as the number of video clips shot of the same area increases. Eliminating LBR improves EOVD coding efficiency considerably. First, this study proposes eliminating LBR by creating a long-term background referencing library (LBRL) containing high-definition geographically registered images of an entire area. Then, it analyzes the factors affecting the variations in the image representations of the background. Next, it proposes a method of generating references for encoding current video and develops the encoding and decoding framework for EOVD compression. Experimental results show that encoding UAV video clips with the proposed method saved an average of more than 54% bits using references generated under the same conditions. Bitrate savings reached 25-35% when applied to satellite video data with arbitrarily collected reference images. Applying the proposed coding method to EOVD will facilitate remote surveillance, which can foster the development of online smart city applications.

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“…Actually, multi-view data contains consistent and complementary information simultaneously across different views [28,31]. Leveraging the complementary information amongst views has better generalization ability than single view [7,23,25,30,46].…”

Section: Introductionmentioning

confidence: 99%

Co-regularized multiview nonnegative matrix factorization with correlation constraint for representation learning

Long

Tan

et al. 2017

Multimed Tools Appl

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With the increasing availability of multiview nonnegative data in real applications, multiview representation learning based on nonnegative matrix factorization (NMF) has attracted more and more attentions. However, existing NMF-based methods are sensitive to noises and are difficult to generate discriminative features with noisy views. To address these problems, we propose a co-regularized multiview nonnegative matrix factorization method with correlation constraint for nonnegative representation learning, which jointly exploits consistent and complementary information across different views. Different from previous works, we aim at integrating information from multiple views efficiently and making it more robust to the presence of noisy views. More specifically, we exploit the complementary information of multiple views through the co-regularization to accommodate the presence of the noisy views. Meanwhile, correlation constraint is imposed on the low-dimensional space to learn a common latent representation shared by different views. For the induced objective function, we derive an alternative algorithm to solve the optimization problem. The experimental results on four real datasets demonstrate the effectiveness and robustness of the proposed algorithm.

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Multi-view low-rank dictionary learning for image classification

Cited by 121 publications

References 45 publications

Enhancing the representation of functional connectivity networks by fusing multi‐view information for autism spectrum disorder diagnosis

Enhancing the representation of functional connectivity networks by fusing multi‐view information for autism spectrum disorder diagnosis

Towards Real-Time Service from Remote Sensing: Compression of Earth Observatory Video Data via Long-Term Background Referencing

Co-regularized multiview nonnegative matrix factorization with correlation constraint for representation learning

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