Auto-Weighted Multi-View Learning for Image Clustering and Semi-Supervised Classification

Nie, Feiping; Cai, Guohao; Li, Jing; Li, Xuelong

doi:10.1109/tip.2017.2754939

Cited by 385 publications

(221 citation statements)

References 31 publications

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“…To evaluate the performance of our method, we compared results of nine related algorithms. To highlight the performance of the proposed multi-view features joint learning and fusion method, we compared it with three representative multi-view learning methods, i.e., adaptively weighted procrustes (AWP) [26], automatic multi-view graph and weights learning (AMGL) [23] and multi-view learning with adaptive neighbors (MLAN) [25]. AWP is an unsupervised method, which is compared with AMGL, MLAN and our method to show the performance of multi-view unsupervised and supervised classification methods.…”

Section: The Second Experimental Group (1) Comparison Methodsmentioning

confidence: 99%

“…Different view features describe properties of different aspects of a point, but they commonly represent the same point. Typically, the multi-view learning methods [23][24][25][26][27][28][29][30][31] can effectively fuse features from different views. The authors leverage this diversity and consistency of different view features to obtain more discriminative feature representation.…”

Section: Introductionmentioning

confidence: 99%

“…The authors leverage this diversity and consistency of different view features to obtain more discriminative feature representation. More specifically, Nie et al [25] proposed an adaptive weighted multi-view learning algorithm (MLAN) for image clustering and semi-supervised classification.…”

Section: Introductionmentioning

confidence: 99%

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Multi-View Features Joint Learning with Label and Local Distribution Consistency for Point Cloud Classification

Tong

Chen

et al. 2020

Remote Sensing

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In outdoor Light Detection and Ranging (lidar)point cloud classification, finding the discriminative features for point cloud perception and scene understanding represents one of the great challenges. The features derived from defect-laden (i.e., noise, outliers, occlusions and irregularities) and raw outdoor LiDAR scans usually contain redundant and irrelevant information which adversely affects the accuracy of point semantic labeling. Moreover, point cloud features of different views have a capability to express different attributes of the same point. The simplest way of concatenating these features of different views cannot guarantee the applicability and effectiveness of the fused features. To solve these problems and achieve outdoor point cloud classification with fewer training samples, we propose a novel multi-view features and classifiers’ joint learning framework. The proposed framework uses label consistency and local distribution consistency of multi-space constraints for multi-view point cloud features extraction and classification. In the framework, the manifold learning is used to carry out subspace joint learning of multi-view features by introducing three kinds of constraints, i.e., local distribution consistency of feature space and position space, label consistency among multi-view predicted labels and ground truth, and label consistency among multi-view predicted labels. The proposed model can be well trained by fewer training points, and an iterative algorithm is used to solve the joint optimization of multi-view feature projection matrices and linear classifiers. Subsequently, the multi-view features are fused and used for point cloud classification effectively. We evaluate the proposed method on five different point cloud scenes and experimental results demonstrate that the classification performance of the proposed method is at par or outperforms the compared algorithms.

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Section: The Second Experimental Group (1) Comparison Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Multi-View Features Joint Learning with Label and Local Distribution Consistency for Point Cloud Classification

Tong

Chen

et al. 2020

Remote Sensing

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“…Inspired with these works [34,43,46], we realize that different view plays different importance in learning to obtain the low-dimensional embedding. Thus, we allocate the different weights for different views, which reflect the importance that each view plays in learning to obtain the low-dimensional embedding.…”

Section: B Multi-view Framework Based On Similarity Consensusmentioning

confidence: 99%

The similarity-consensus regularized multi-view learning for dimension reduction

Meng

Wang

Feng

2020

Knowledge-Based Systems

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During the last decades, learning a low-dimensional space with discriminative information for dimension reduction (DR) has gained a surge of interest. However, it's not accessible for these DR methods to achieve satisfactory performance when facing the features from multiple views. In multi-view learning problems, one instance can be represented by multiple heterogeneous features, which are highly related but sometimes look different from each other. In addition, correlations between features from multiple views always vary greatly, which challenges the capability of multi-view learning methods. Consequently, constructing a multi-view learning framework with generalization and scalability, which could take advantage of multi-view information as much as possible, is extremely necessary but challenging. To implement the above target, this paper proposes a novel multiview learning framework based on similarity consensus, which makes full use of correlations among multi-view features while considering the scalability and robustness of the framework. It aims to straightforwardly extend those existing DR methods into multi-view learning domain by preserving the similarity between different views to capture the low-dimensional embedding. Two schemes based on pairwise-consensus and centroid-consensus are separately proposed to force multiple views to learn from each other and then an iterative alternating strategy is developed to obtain the optimal solution. The proposed method is evaluated on 5 benchmark datasets and comprehensive experiments show that our proposed multi-view framework can yield comparable and promising performance with previous approaches proposed in recent literatures.

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“…The model above needs a manually specified parameter γ to adjust the weights of different views, which is sometimes intractable. Thus, Nie et al [31] propose a parameter-free autoweighted multiple graph learning method (AMGL), wherein the weight parameter…”

Section: Variants Of Multi-view Spectral Clusteringmentioning

confidence: 99%

New Approaches in Multi-View Clustering

Ye¹,

Chen²,

Qian³

et al. 2018

Recent Applications in Data Clustering

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Many real-world datasets can be naturally described by multiple views. Due to this, multiview learning has drawn much attention from both academia and industry. Compared to single-view learning, multi-view learning has demonstrated plenty of advantages. Clustering has long been serving as a critical technique in data mining and machine learning. Recently, multi-view clustering has achieved great success in various applications. To provide a comprehensive review of the typical multi-view clustering methods and their corresponding recent developments, this chapter summarizes five kinds of popular clustering methods and their multi-view learning versions, which include k-means, spectral clustering, matrix factorization, tensor decomposition, and deep learning. These clustering methods are the most widely employed algorithms for single-view data, and lots of efforts have been devoted to extending them for multi-view clustering. Besides, many other multi-view clustering methods can be unified into the frameworks of these five methods. To promote further research and development of multi-view clustering, some popular and open datasets are summarized in two categories. Furthermore, several open issues that deserve more exploration are pointed out in the end.

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Auto-Weighted Multi-View Learning for Image Clustering and Semi-Supervised Classification

Cited by 385 publications

References 31 publications

Multi-View Features Joint Learning with Label and Local Distribution Consistency for Point Cloud Classification

Multi-View Features Joint Learning with Label and Local Distribution Consistency for Point Cloud Classification

The similarity-consensus regularized multi-view learning for dimension reduction

New Approaches in Multi-View Clustering

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