scholarly journals State of the Art: Coordinated & Multiple Views in Exploratory Visualization

2007 ◽  
Author(s):  
Jonathan C. Roberts
Keyword(s):  
State Of The Art ◽  
Multiple Views

scholarly journals Semi-Supervised Multi-View Clustering with Weighted Anchor Graph Embedding

2021 ◽  
Vol 2021 ◽  
pp. 1-22
Author(s):  
Senhong Wang ◽  
Jiangzhong Cao ◽  
Fangyuan Lei ◽  
Qingyun Dai ◽  
Shangsong Liang ◽  
...  
Keyword(s):  
State Of The Art ◽  
Graph Embedding ◽  
Multiple Views ◽  
Time Cost ◽  
Practical Application ◽  
Real World Data ◽  
High Quality ◽  
Free Graph ◽  
Anchor Points ◽  
Almost All

A number of literature reports have shown that multi-view clustering can acquire a better performance on complete multi-view data. However, real-world data usually suffers from missing some samples in each view and has a small number of labeled samples. Additionally, almost all existing multi-view clustering models do not execute incomplete multi-view data well and fail to fully utilize the labeled samples to reduce computational complexity, which precludes them from practical application. In view of these problems, this paper proposes a novel framework called Semi-supervised Multi-View Clustering with Weighted Anchor Graph Embedding (SMVC_WAGE), which is conceptually simple and efficiently generates high-quality clustering results in practice. Specifically, we introduce a simple and effective anchor strategy. Based on selected anchor points, we can exploit the intrinsic and extrinsic view information to bridge all samples and capture more reliable nonlinear relations, which greatly enhances efficiency and improves stableness. Meanwhile, we construct the global fused graph compatibly across multiple views via a parameter-free graph fusion mechanism which directly coalesces the view-wise graphs. To this end, the proposed method can not only deal with complete multi-view clustering well but also be easily extended to incomplete multi-view cases. Experimental results clearly show that our algorithm surpasses some state-of-the-art competitors in clustering ability and time cost.

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

2021 ◽  
Vol 8 ◽  
Author(s):  
Shuqin Wang ◽  
Yongyong Chen ◽  
Fangying Zheng
Keyword(s):  
Augmented Lagrangian ◽  
State Of The Art ◽  
Augmented Lagrangian Method ◽  
Subspace Clustering ◽  
Low Rank ◽  
Multiple Views ◽  
Clustering Methods ◽  
Tensor Representation ◽  
Numerical Studies ◽  
Rank Tensor

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.

scholarly journals An Actor-Critic-Attention Mechanism for Deep Reinforcement Learning in Multi-view Environments

2019 ◽  
Author(s):  
Elaheh Barati ◽  
Xuewen Chen
Keyword(s):  
Reinforcement Learning ◽  
State Of The Art ◽  
Attention Mechanism ◽  
Feature Representation ◽  
Decision Making Process ◽  
Multiple Views ◽  
Single Feature ◽  
3D Environments ◽  
Noisy Conditions ◽  
Level Of Importance

In reinforcement learning algorithms, leveraging multiple views of the environment can improve the learning of complicated policies. In multi-view environments, due to the fact that the views may frequently suffer from partial observability, their level of importance are often different. In this paper, we propose a deep reinforcement learning method and an attention mechanism in a multi-view environment. Each view can provide various representative information about the environment. Through our attention mechanism, our method generates a single feature representation of environment given its multiple views. It learns a policy to dynamically attend to each view based on its importance in the decision-making process. Through experiments, we show that our method outperforms its state-of-the-art baselines on TORCS racing car simulator and three other complex 3D environments with obstacles. We also provide experimental results to evaluate the performance of our method on noisy conditions and partial observation settings.

scholarly journals Unified Embedding Alignment with Missing Views Inferring for Incomplete Multi-View Clustering

2019 ◽  
Vol 33 ◽  
pp. 5393-5400 ◽  
Author(s):  
Jie Wen ◽  
Zheng Zhang ◽  
Yong Xu ◽  
Bob Zhang ◽  
Lunke Fei ◽  
...  
Keyword(s):  
Real World ◽  
Local Structure ◽  
Semantic Information ◽  
State Of The Art ◽  
Multiple Views ◽  
Learning Problem ◽  
Graph Regularization ◽  
Real World Applications ◽  
Weighting Strategy ◽  
The Common

Multi-view clustering aims to partition data collected from diverse sources based on the assumption that all views are complete. However, such prior assumption is hardly satisfied in many real-world applications, resulting in the incomplete multi-view learning problem. The existing attempts on this problem still have the following limitations: 1) the underlying semantic information of the missing views is commonly ignored; 2) The local structure of data is not well explored; 3) The importance of different views is not effectively evaluated. To address these issues, this paper proposes a Unified Embedding Alignment Framework (UEAF) for robust incomplete multi-view clustering. In particular, a locality-preserved reconstruction term is introduced to infer the missing views such that all views can be naturally aligned. A consensus graph is adaptively learned and embedded via the reverse graph regularization to guarantee the common local structure of multiple views and in turn can further align the incomplete views and inferred views. Moreover, an adaptive weighting strategy is designed to capture the importance of different views. Extensive experimental results show that the proposed method can significantly improve the clustering performance in comparison with some state-of-the-art methods.

scholarly journals Consensus Kernel K-Means Clustering for Incomplete Multiview Data

2017 ◽  
Vol 2017 ◽  
pp. 1-11 ◽  
Author(s):  
Yongkai Ye ◽  
Xinwang Liu ◽  
Qiang Liu ◽  
Jianping Yin
Keyword(s):  
Real World ◽  
State Of The Art ◽  
Clustering Algorithms ◽  
Multiple Views ◽  
Learning Method ◽  
Learning Framework ◽  
Optimal Integration ◽  
Art Methods ◽  
Multiview Clustering

Multiview clustering aims to improve clustering performance through optimal integration of information from multiple views. Though demonstrating promising performance in various applications, existing multiview clustering algorithms cannot effectively handle the view’s incompleteness. Recently, one pioneering work was proposed that handled this issue by integrating multiview clustering and imputation into a unified learning framework. While its framework is elegant, we observe that it overlooks the consistency between views, which leads to a reduction in the clustering performance. In order to address this issue, we propose a new unified learning method for incomplete multiview clustering, which simultaneously imputes the incomplete views and learns a consistent clustering result with explicit modeling of between-view consistency. More specifically, the similarity between each view’s clustering result and the consistent clustering result is measured. The consistency between views is then modeled using the sum of these similarities. Incomplete views are imputed to achieve an optimal clustering result in each view, while maintaining between-view consistency. Extensive comparisons with state-of-the-art methods on both synthetic and real-world incomplete multiview datasets validate the superiority of the proposed method.

scholarly journals Multi-View Collaborative Network Embedding

2021 ◽  
Vol 15 (3) ◽  
pp. 1-18
Author(s):  
Sezin Kircali Ata ◽  
Yuan Fang ◽  
Min Wu ◽  
Jiaqi Shi ◽  
Chee Keong Kwoh ◽  
...  
Keyword(s):  
Real World ◽  
State Of The Art ◽  
Second Order ◽  
Collaborative Network ◽  
Multiple Views ◽  
Network Embedding ◽  
Single View ◽  
Video Sharing ◽  
Low Dimensional

Real-world networks often exist with multiple views, where each view describes one type of interaction among a common set of nodes. For example, on a video-sharing network, while two user nodes are linked, if they have common favorite videos in one view, then they can also be linked in another view if they share common subscribers. Unlike traditional single-view networks, multiple views maintain different semantics to complement each other. In this article, we propose M ulti-view coll A borative N etwork E mbedding (MANE), a multi-view network embedding approach to learn low-dimensional representations. Similar to existing studies, MANE hinges on diversity and collaboration—while diversity enables views to maintain their individual semantics, collaboration enables views to work together. However, we also discover a novel form of second-order collaboration that has not been explored previously, and further unify it into our framework to attain superior node representations. Furthermore, as each view often has varying importance w.r.t. different nodes, we propose MANE , an attention -based extension of MANE, to model node-wise view importance. Finally, we conduct comprehensive experiments on three public, real-world multi-view networks, and the results demonstrate that our models consistently outperform state-of-the-art approaches.

scholarly journals Learning Single-Image 3D Reconstruction by Generative Modelling of Shape, Pose and Shading

2019 ◽  
Vol 128 (4) ◽  
pp. 835-854 ◽  
Author(s):  
Paul Henderson ◽  
Vittorio Ferrari
Keyword(s):  
3D Reconstruction ◽  
State Of The Art ◽  
Multiple Views ◽  
Single Image ◽  
Unified Framework ◽  
Single View ◽  
Supervised Methods ◽  
Generative Modelling ◽  
2D Images ◽  
Better Than

Abstract We present a unified framework tackling two problems: class-specific 3D reconstruction from a single image, and generation of new 3D shape samples. These tasks have received considerable attention recently; however, most existing approaches rely on 3D supervision, annotation of 2D images with keypoints or poses, and/or training with multiple views of each object instance. Our framework is very general: it can be trained in similar settings to existing approaches, while also supporting weaker supervision. Importantly, it can be trained purely from 2D images, without pose annotations, and with only a single view per instance. We employ meshes as an output representation, instead of voxels used in most prior work. This allows us to reason over lighting parameters and exploit shading information during training, which previous 2D-supervised methods cannot. Thus, our method can learn to generate and reconstruct concave object classes. We evaluate our approach in various settings, showing that: (i) it learns to disentangle shape from pose and lighting; (ii) using shading in the loss improves performance compared to just silhouettes; (iii) when using a standard single white light, our model outperforms state-of-the-art 2D-supervised methods, both with and without pose supervision, thanks to exploiting shading cues; (iv) performance improves further when using multiple coloured lights, even approaching that of state-of-the-art 3D-supervised methods; (v) shapes produced by our model capture smooth surfaces and fine details better than voxel-based approaches; and (vi) our approach supports concave classes such as bathtubs and sofas, which methods based on silhouettes cannot learn.

scholarly journals Multi-View Attention Network for Visual Dialog

2021 ◽  
Vol 11 (7) ◽  
pp. 3009
Author(s):  
Sungjin Park ◽  
Taesun Whang ◽  
Yeochan Yoon ◽  
Heuiseok Lim
Keyword(s):  
State Of The Art ◽  
Relevant Information ◽  
Experimental Results ◽  
Multiple Views ◽  
Single Model ◽  
Attention Network ◽  
Proposed Model ◽  
Previous State ◽  
Multimodal Representations ◽  
High Level

Visual dialog is a challenging vision-language task in which a series of questions visually grounded by a given image are answered. To resolve the visual dialog task, a high-level understanding of various multimodal inputs (e.g., question, dialog history, and image) is required. Specifically, it is necessary for an agent to (1) determine the semantic intent of question and (2) align question-relevant textual and visual contents among heterogeneous modality inputs. In this paper, we propose Multi-View Attention Network (MVAN), which leverages multiple views about heterogeneous inputs based on attention mechanisms. MVAN effectively captures the question-relevant information from the dialog history with two complementary modules (i.e., Topic Aggregation and Context Matching), and builds multimodal representations through sequential alignment processes (i.e., Modality Alignment). Experimental results on VisDial v1.0 dataset show the effectiveness of our proposed model, which outperforms previous state-of-the-art methods under both single model and ensemble settings.

scholarly journals Multi-view Knowledge Graph Embedding for Entity Alignment

2019 ◽  
Author(s):  
Qingheng Zhang ◽  
Zequn Sun ◽  
Wei Hu ◽  
Muhao Chen ◽  
Lingbing Guo ◽  
...  
Keyword(s):  
Performance Improvement ◽  
Real World ◽  
State Of The Art ◽  
Graph Embedding ◽  
Knowledge Graph ◽  
Multiple Views ◽  
Combination Strategies ◽  
Knowledge Graphs ◽  
Real World Datasets ◽  
Inference Methods

We study the problem of embedding-based entity alignment between knowledge graphs (KGs). Previous works mainly focus on the relational structure of entities. Some further incorporate another type of features, such as attributes, for refinement. However, a vast of entity features are still unexplored or not equally treated together, which impairs the accuracy and robustness of embedding-based entity alignment. In this paper, we propose a novel framework that unifies multiple views of entities to learn embeddings for entity alignment. Specifically, we embed entities based on the views of entity names, relations and attributes, with several combination strategies. Furthermore, we design some cross-KG inference methods to enhance the alignment between two KGs. Our experiments on real-world datasets show that the proposed framework significantly outperforms the state-of-the-art embedding-based entity alignment methods. The selected views, cross-KG inference and combination strategies all contribute to the performance improvement.

Practical Picture Processing

1974 ◽  
Vol 32 ◽  
pp. 338-339
Author(s):  
T. A. Welton
Keyword(s):  
Radiation Damage ◽  
Coherence Length ◽  
Spatial Information ◽  
State Of The Art ◽  
Coherent Radiation ◽  
The State ◽  
Energy Spread ◽  
Electron Micrograph ◽  
Picture Processing ◽  
Molecular Skeleton

Various authors have emphasized the spatial information resident in an electron micrograph taken with adequately coherent radiation. In view of the completion of at least one such instrument, this opportunity is taken to summarize the state of the art of processing such micrographs. We use the usual symbols for the aberration coefficients, and supplement these with £ and 6 for the transverse coherence length and the fractional energy spread respectively. He also assume a weak, biologically interesting sample, with principal interest lying in the molecular skeleton remaining after obvious hydrogen loss and other radiation damage has occurred.

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