Kernelized Multiview Subspace Analysis By Self-Weighted Learning

Wang, Huibing; Wang, Yan; Zhang, Zhao; Fu, Xianping; Li, Zhuo; Xu, Mingliang; Wang, Meng

doi:10.1109/tmm.2020.3032023

Cited by 106 publications

(32 citation statements)

References 37 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In order to overcome these limitations, we propose a novel weighted low-rank tensor representation (WLRTR) method, which uses Tucker decomposition to simplify the calculation of the tensor nuclear norm and assigns different weights to the core tensor to take advantage of the main information in different views. The proposed WLRTR is formulated as: 1) ; E (2) ; /; E (V) ,…”

Section: Model Formulationmentioning

confidence: 99%

“…Therefore, the Eq. 4 can be transformed into the following formulation: 1) ; E (2) ; /; E (V) , Z Y.…”

Section: Optimization Of Wlrtrmentioning

confidence: 99%

“…Recently, [1] applied multi-view binary learning to obtain the supplementary information from multiple views. [2] proposed a kernelized multi-view subspace analysis method via self-weighted learning. Due to the lack of label, clustering using multiple views has become a popular research direction [3].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Weighted Low-Rank Tensor Representation for Multi-View Subspace Clustering

2021

View full text Add to dashboard Cite

Multi-view clustering has been deeply explored since the compatible and complementary information among views can be well captured. Recently, the low-rank tensor representation-based methods have effectively improved the clustering performance by exploring high-order correlations between multiple views. However, most of them often express the low-rank structure of the self-representative tensor by the sum of unfolded matrix nuclear norms, which may cause the loss of information in the tensor structure. In addition, the amount of effective information in all views is not consistent, and it is unreasonable to treat their contribution to clustering equally. To address the above issues, we propose a novel weighted low-rank tensor representation (WLRTR) method for multi-view subspace clustering, which encodes the low-rank structure of the representation tensor through Tucker decomposition and weights the core tensor to retain the main information of the views. Under the augmented Lagrangian method framework, an iterative algorithm is designed to solve the WLRTR method. Numerical studies on four real databases have proved that WLRTR is superior to eight state-of-the-art clustering methods.

show abstract

Section: Model Formulationmentioning

confidence: 99%

“…Therefore, the Eq. 4 can be transformed into the following formulation: 1) ; E (2) ; /; E (V) , Z Y.…”

Section: Optimization Of Wlrtrmentioning

confidence: 99%

See 1 more Smart Citation

Weighted Low-Rank Tensor Representation for Multi-View Subspace Clustering

2021

View full text Add to dashboard Cite

show abstract

“…The goal of multi-view learning is to effectively utilize the local manifold of multiple views for extracting a coherent low-dimensional representation of the different feature data. Some researchers pay more attentions to exploit a low-dimensional subspace by dimensionality reduction(DR) methods, others focus on incorporating multi-view features to achieve more excellent performance in real applications [30], [31]. Such as multi-view spectral embedding(MSE) [32], multi-view discriminant analysis (MvDA) [33], co-training approach for multi-view spectral clustering(Co-trained) [34].…”

Section: Introductionmentioning

confidence: 99%

A Multi-View Fusion Method via Tensor Learning and Gradient Descent for Image Features

Zhang

Liu

et al. 2021

IEEE Access

View full text Add to dashboard Cite

In many computer vision applications, one image can be represented by multiple heterogeneous features from different views, most of them commonly locate in high-dimensional space. These features can reflect different characteristics of one same object, they contain compatible and complementary information among each other. How to construct an uniform low-dimensional embedding features which represent useful information of multi-view features is still an important and urgent issue to be solved. Therefore, we propose a multi-view fusion method via tensor learning and gradient descent (MvF-TG) in this paper. MvF-TG reconstructs a low dimensional mapping subspace of each object by utilizing its k nearest neighbors, which preserves the underlying neighborhood structure of the original local manifold. The new method can effectively exploit the spatial correlation information from the multi-view features by tensor learning. Furthermore, the method constructs a gradient descent optimization model to generate the better unified low dimensional embedding. The proposed method is compared with several single-view and multi-view dimensional reduction methods in these indicators of P, R, MAP and F-measure. In the retrieval experiments, the P values of the new method respectively are 86.80%, 52.00%, 68.56% and 78.80% on datasets of Corel1k, Corel5k, Corel10k and Holidays. In the classification experiments, the mean accuracies of it respectively are 47.94% and 87.58% on datasets of Caltech101 and Coil. These values are higher than those obtained by other comparison methods, various evaluations based on the applications of image classification and retrieval demonstrate the effectiveness of our proposed method on multi-view feature fusion dimension reduction.

show abstract

“…Nowadays, cross-modal retrieval [34,36,41] has become a hot issue in the community of multimedia [30] due to the multi-modal data (image, text, audio, video) increase explosively. It is a critical but very challenging problem that aims to find out samples of one modality from a multimedia database by queries of other different modalities.…”

Section: Introductionmentioning

confidence: 99%

M2guda

Zhang

Zhi

Zhu

et al. 2021

Proceedings of the 2021 International Conference on Multimedia Retrieval

View full text Add to dashboard Cite

Cross-modal hashing is a critical but very challenging task that is to retrieve similar samples of one modality via queries of other modalities. To improve the unsupervised cross-modal hashing, domain adaptation techniques can be used to support unsupervised hashing learning by transferring semantic knowledge from labeled source domain to unlabeled target domain. However, there are two problems that cannot be ignored: (1) most of domain adaptation based researches mainly focused on unimodal hashing or cross-modal real value-based retrieval but the study for cross-modal hashing is limited; (2) most existing studies only consider one or two consistency constraints during the domain adaptation learning. To this end, this paper propose a novel end-to-end framework to realize unsupervised domain adaptation for cross-modal hashing. This method, dubbed M 2 GUDA, including four different consistency constraints: structure consistency, domain consistency, semantic consistency and modality consistency for domain adaptation learning. Besides, to enhance the structure consistency learning, we develop a multimetrics graph modeling method to capture structure information comprehensively. Extensive experiments are performed on three common used benchmarks to evaluate the effectivity of our method. The results show that our method outperforms several state-of-theart cross-modal hashing methods. CCS CONCEPTS• Information systems → Multimedia and multimodal retrieval.

show abstract

Kernelized Multiview Subspace Analysis By Self-Weighted Learning

Cited by 106 publications

References 37 publications

Weighted Low-Rank Tensor Representation for Multi-View Subspace Clustering

Weighted Low-Rank Tensor Representation for Multi-View Subspace Clustering

A Multi-View Fusion Method via Tensor Learning and Gradient Descent for Image Features

M2guda

Contact Info

Product

Resources

About