Multi-view Classification Method Research Articles

Deep Incomplete Multi-View Learning Network with Insufficient Label Information

Due to the efficiency of integrating semantic consensus and complementary information across different views, multi-view classification methods have attracted much attention in recent years. However, multi-view data often suffers from both the miss of view features and insufficient label information, which significantly decrease the performance of traditional multi-view classification methods in practice. Learning for such simultaneous lack of feature and label is crucial but rarely studied. To tackle these problems, we propose a novel Deep Incomplete Multi-view Learning Network (DIMvLN) by incorporating graph networks and semi-supervised learning in this paper. Specifically, DIMvLN firstly designs the deep graph networks to effectively recover missing data with assigning pseudo-labels of large amounts of unlabeled instances and refine the incomplete feature information. Meanwhile, to enhance the label information, a novel pseudo-label generation strategy with the similarity constraints of unlabeled instances is proposed to exploit additional supervisory information and guide the completion module to preserve more semantic information of absent multi-view data. Besides, we design view-specific representation extractors with the autoencoder structure and contrastive loss to learn high-level semantic representations for each view, promote cross-view consistencies and augment the separability between different categories. Finally, extensive experimental results demonstrate the effectiveness of our DIMvLN, attaining noteworthy performance improvements compared to state-of-the-art competitors on several public benchmark datasets. Code will be available at GitHub.

Sample-weighted fused graph-based semi-supervised learning on multi-view data

Research in semi-supervised learning on graphs has attracted more and more attention in recent years, as learning on graphs is applied in more and more domains and labeling data is expensive and time-consuming. Some scenarios have inherent graph structures in their data, such as the relationships between people in social scenarios or the relationships between objects that are mutually referenced. However, there are also many data types without inherent graph structures, such as image data, and each image can be described with different features, which is a typical type of multi-view data. For image data and other non-graph data, there are significantly fewer deep learning approaches that target multi-view graph-based semi-supervised learning. This paper attempts to fill this gap. Based on the Graph Convolutional Network (GCN) architecture, we propose a Sample-weighted Fused Graph-based Semi-supervised Classification (WFGSC) method for multi-view data in this paper. It (i) constructs a semi-supervised graph in each view using a flexible model for joint graph and label estimation, (ii) obtains an additional graph based on the representation of nodes provided by the joint estimator, and then obtains a fused graph between all views, (iii) gives higher weights to hard-to-classify samples, (iv) proposes a loss function to train the GCN on the fused features and the consensus graph that integrates graph auto-encoder loss and label smoothing over the consensus graph. The results of our experiments on six multi-view datasets show that our WFGSC performs well on both fused graph construction and semi-supervised classification, and generally outperforms traditional GCNs and other multi-view semi-supervised multi-view classification methods.11Source code: https://github.com/BiJingjun/WFGSC.

When multi-view classification meets ensemble learning

With the coming of big data era, multi-view data represented by multiple features have been involved in many terms, such as machine learning, data mining and computer vision and so on. Due to the complex structure hidden in data, how to utilize the complementary and correlative information among multiple view features to improve classification performance is a challenging task. Moreover, the another challenging task is how to assign an appropriate weight for each classifier on the basis of its performance. To solve above problems, we proposed a supervised multi-view classification method based on Least Square Regression (LSR) and Ensemble Learning. To be specific, all samples for each view firstly can be classified by using Multi-class Support Vector Machine (MSVM); Then, to evaluate the classification results of different views for each sample, the optimal weight of each sample classification result is learned; Furthermore, considering the view difference with different classification quality, the view weights are assigned adaptively; Finally, we adopt the decision function values to determine the final classification results. Extensive experimental results show that the proposed method outperforms most state-of-the-art multi-view classification methods.

Semi-supervised learning based on intra-view heterogeneity and inter-view compatibility for image classification

To achieve high value of classification results by using a limited number of training samples, designing a multi-view semi-supervised classification model is extremely urgent. Moreover, considering that graph learning can fully capture the complex data structure information, we propose a graph-based multi-view semi-supervised classification approach named as Semi-supervised Classification based on Intra-view Heterogeneity and Inter-view Compatibility (SSC_IHIC). Specifically, the similarity between two samples can be measured based on the Euclidean distance. Considering that heterogeneity exists in intra-view sub-features, hence, the weights are learned adaptively. Besides, based on the inter-view compatibility, the consensus similarity graph is constructed on which the labels are propagated. To verify the effectiveness, the proposed approach is conducted on four data sets including MSRC-v1, Handwritten (HW), Cal101-7 and Cal101-20. From the experimental results, we can see that the classification performance about proposed method outperforms the single view classification methods and the state-of-the-art multi-view classification methods.

2D Multi-view Discriminant Analysis

With the advent of technology, we have seen an explosion in high-definition images. However, due to the large size of images, 1D methods are less suitable to handle such data. This issue becomes even more pronounced when working with multi-view datasets. We propose a generalized method, 2D Multi-view Discriminant Analysis (2DMvDA), to tackle this issue. 2DMvDA is a 2D multi-view classification method based on discriminant analysis. It uses the 2D image matrices directly instead of extracting 1D features from them, leading to a considerable reduction in the size of the data to be processed. We compare the proposed method with 1D Multi-view Discriminant Analysis (MvDA) and single-view 2D Linear Discriminant Analysis (2DLDA). We experimentally show that the proposed method requires less than 0.1% training time and memory than MvDA. Also, though it needs the same memory, it requires 30–70% less training time than 2DLDA. Using less time and memory, 2DMvDA achieves a classification accuracy that is at par or better than both of these methods.

Supervised multi-view classification via the sparse learning joint the weighted elastic loss

Multi-view data contains the global shared information of all views and the specific information of each view. Usually, the features of all views are concatenated into vectors to be dealt with in the multi-view classification methods, which inevitably ignores the extraction of important views for a specific task and increases the computational complexity. To address the above limitations, we propose a novel supervised classification method via the sparse learning joint the weighted elastic loss. Specifically, for the samples in each class, an elastic sub-model is established by combining the weighted elastic loss and the sparse regularizers. The weighted elastic loss can explore the complementary information of all views and the specific information of each view. The sparse regularizers can select the views that are important to the classification task. Finally, the elastic sub-models of all classes are fused to form the generalized additive classification model. An effective algorithm is designed to optimize the model through solving several small-scale subproblems, which can reduce the computational complexity. The comprehensive evaluations with several advanced methods prove the superiority of the proposed method. Especially on the large-scale Animal dataset, the training time (3.7×103 s) is one time faster than that of the second place (7.5×103 s).

CircRNA-binding protein site prediction based on multi-view deep learning, subspace learning and multi-view classifier.

Circular RNAs (circRNAs) generally bind to RNA-binding proteins (RBPs) to play an important role in the regulation of autoimmune diseases. Thus, it is crucial to study the binding sites of RBPs on circRNAs. Although many methods, including traditional machine learning and deep learning, have been developed to predict the interactions between RNAs and RBPs, and most of them are focused on linear RNAs. At present, few studies have been done on the binding relationships between circRNAs and RBPs. Thus, in-depth research is urgently needed. In the existing circRNA-RBP binding site prediction methods, circRNA sequences are the main research subjects, but the relevant characteristics of circRNAs have not been fully exploited, such as the structure and composition information of circRNA sequences. Some methods have extracted different views to construct recognition models, but how to efficiently use the multi-view data to construct recognition models is still not well studied. Considering the above problems, this paper proposes a multi-view classification method called DMSK based on multi-view deep learning, subspace learning and multi-view classifier for the identification of circRNA-RBP interaction sites. In the DMSK method, first, we converted circRNA sequences into pseudo-amino acid sequences and pseudo-dipeptide components for extracting high-dimensional sequence features and component features of circRNAs, respectively. Then, the structure prediction method RNAfold was used to predict the secondary structure of the RNA sequences, and the sequence embedding model was used to extract the context-dependent features. Next, we fed the above four views' raw features to a hybrid network, which is composed of a convolutional neural network and a long short-term memory network, to obtain the deep features of circRNAs. Furthermore, we used view-weighted generalized canonical correlation analysis to extract four views' common features by subspace learning. Finally, the learned subspace common features and multi-view deep features were fed to train the downstream multi-view TSK fuzzy system to construct a fuzzy rule and fuzzy inference-based multi-view classifier. The trained classifier was used to predict the specific positions of the RBP binding sites on the circRNAs. The experiments show that the prediction performance of the proposed method DMSK has been improved compared with the existing methods. The code and dataset of this study are available at https://github.com/Rebecca3150/DMSK.

New Multi-View Classification Method with Uncertain Data

Multi-view classification aims at designing a multi-view learning strategy to train a classifier from multi-view data, which are easily collected in practice. Most of the existing works focus on multi-view classification by assuming the multi-view data are collected with precise information. However, we always collect the uncertain multi-view data due to the collection process is corrupted with noise in real-life application. In this case, this article proposes a novel approach, called uncertain multi-view learning with support vector machine (UMV-SVM) to cope with the problem of multi-view learning with uncertain data. The method first enforces the agreement among all the views to seek complementary information of multi-view data and takes the uncertainty of the multi-view data into consideration by modeling reachability area of the noise. Then it proposes an iterative framework to solve the proposed UMV-SVM model such that we can obtain the multi-view classifier for prediction. Extensive experiments on real-life datasets have shown that the proposed UMV-SVM can achieve a better performance for uncertain multi-view classification in comparison to the state-of-the-art multi-view classification methods.

Robust Multi-view Classification with Sample Constraints

This paper proposes a new multi-view classification method by taking three sample constraints into account to automatically assign large weights to important samples and small weights to unimportant samples. To do this, we first demonstrate that different samples have different contributions to the classification models, and then propose to consider sample weight, class weight, and view weight, to overcome the influence of different levels of noise. Specifically, the sample weight for every data point is obtained by penalizing an $$\ell _{2,1}$$ -norm loss on its estimation error to reduce the influence of the sample-level noise, the class weight for each class is obtained by considering the misclassification cost as well as imbalance class to overcome the influence of class-level noise, and the view weight for each view is obtained by penalizing a squared root operator on the estimation error of each view to reduce the influence of view-level noise. In particular, our proposed sample constraints can be easily embedded in previous multi-view learning models. Experimental results on simulated and real data sets showed that our proposed method was superior to the state-of-the-art classification methods in terms of classification performance of cost-sensitive learning.

Multi-view classification with semi-supervised learning for SAR target recognition

A large number of labeled samples are required for convolutional neural network (CNN) to train a deep network model with satisfactory generalization ability. However, it is fairly expensive to obtain sufficient labeled samples in most synthetic aperture radar (SAR) applications. To deal with the problem, in this paper, we propose a novel multi-view classification method with semi-supervised learning for SAR target recognition, which mainly contains a CNN model with the label propagation ability (CNN-LP) and a new expectation maximization (EM) based multi-view fusion strategy. In the training phase, an initial CNN model is trained with limited labeled samples, which is further used to assign pseudo labels for unlabeled samples by the label propagation. Then we can obtain a robust CNN-LP model by alternately updating the model and propagating labels. In the testing phase, the CNN-LP model is used to generate the classification probabilities. To further alleviate the sensitivity of the model towards large depression angle variations, we construct a multi-view label set (MLS) by selecting possible labels adaptively according to the predicted probabilities. Finally, a new EM-based strategy is designed to give the predicted labels. Unlike most of existing multi-view methods which have strict constraints on the angle interval among multiple views, the proposed strategy is free from the aspect interval limitation. Experiments conducted on different datasets all demonstrate the robustness and effectiveness of the proposed method for SAR target recognition.

Tensor-based restricted kernel machines for multi-view classification

Multi-view learning deals with data that is described through multiple representations, or views. While various real-world data can be represented by three or more views, several existing multi-view classification methods can only handle two views. Previously proposed methods usually solve this issue by optimizing pairwise combinations of views. Although this can numerically deal with the issue of multiple views, it ignores the higher order correlations which can only be examined by exploring all views simultaneously. In this work new multi-view classification approaches are introduced which aim to include higher order statistics when three or more views are available. The proposed model is an extension to the recently proposed Restricted Kernel Machine classifier model and assumes shared hidden features for all views, as well as a newly introduced model tensor. Experimental results show an improvement with respect to state-of-the art pairwise multi-view learning methods, both in terms of classification accuracy and runtime.

Multi-View Scaling Support Vector Machines for Classification and Feature Selection

With the explosive growth of data, the multi-view data is widely used in many fields, such as data mining, machine learning, computer vision, and so on. Because such data always has a complex structure, i.e., many categories, many perspectives of description and high dimension, how to formulate an accurate and reliable framework for the multi-view classification is a very challenging task. In this paper, we propose a novel multi-view classification method by using multiple multi-class Support Vector Machines (SVMs) with a novel collaborative strategy. Here, each multi-class SVM embeds the scaling factor to renewedly adjust the weight allocation of all features, which is beneficial to highlight more important and discriminative features. Furthermore, we adopt the decision function values to integrate multiple multi-class learners and introduce the confidence score across multiple classes to determine the final classification result. In addition, through a series of the mathematical deduction, we bridge the proposed model with the solvable problem and solve it through an alternating iteration optimization method. We evaluate the proposed method on several image and face datasets, and the experimental results demonstrate that our proposed method performs better than other state-of-the-art learning algorithms.

Multi-view RBM with posterior consistency and domain adaptation

The restricted Boltzmann machine (RBM) and extensions are rarely used in the field of multi-view learning. In this paper, we first present a multi-view RBM model for classification, which is named RBM with posterior consistency (PCRBM). PCRBM computes multiple representations by regularizing the marginal likelihood function with the consistency among representations from multiple views. However, PCRBM ignores the specific information of each view, where the learned representations just contain the consistency information among multiple views. To address this problem, we propose a novel multi-view RBM model and name it as RBM with posterior consistency and domain adaptation (PDRBM). PDRBM divides the hidden units of each view into two groups: the one is the view-consistency group that contains the consistency information among multiple views, and the other is the view-specific group that contains the information unique to this separate view. In addition, PDRBM balances the relationship among view-consistency hidden representations for multi-view classification. Contrasting with existing multi-view classification methods, PDRBM has achieved satisfactory results on two-class and multi-class classification multi-view datasets.

Multimodal correlation deep belief networks for multi-view classification

The Restricted Boltzmann machine (RBM) has been proven to be a powerful tool in many specific applications, such as representational learning, document modeling, and many other learning tasks. However, the extensions of the RBM are rarely used in the field of multi-view learning. In this paper, we present a new RBM model based on canonical correlation analysis, named as the correlation RBM, for multi-view learning. The correlation RBM computes multiple representations by regularizing the marginal likelihood function with the consistency among representations from different views. In addition, the multimodal deep model can obtain a unified representation that fuses multiple representations together. Therefore, we stack the correlation RBM to create the correlation deep belief network (DBN), and then propose the multimodal correlation DBN for learning multi-view data representations. Contrasting with existing multi-view classification methods, such as multi-view Gaussian process with posterior consistency (MvGP) and consensus and complementarity based maximum entropy discrimination (MED-2C), the correlation RBM and the multimodal correlation DBN have achieved satisfactory results on two-class and multi-class classification datasets. Experimental results show that correlation RBM and the multimodal correlation DBN are effective learning algorithms.

Multi-View Least Squares Support Vector Machines Classification

Abstract In multi-view learning, data is described using different representations, or views. Multi-view classification methods try to exploit information from all views to improve the classification performance. Here a new model is proposed that performs classification when two or more views are available. The model is called Multi-View Least Squares Support Vector Machines (MV-LSSVM) Classification and is based on solving a constrained optimization problem. The primal objective includes a coupling term, which minimizes a combination of the errors from all views. The model combines the benefits from both early and late fusion, it is able to incorporate information from all views in the training phase while still allowing for some degree of freedom to model the views differently. Experimental comparisons with similar methods show that using multiple views improves the results with regard to the single view classifiers and that it outperforms other state-of-the-art algorithms in terms of classification accuracy.

Consensus and complementarity based maximum entropy discrimination for multi-view classification

Maximum entropy discrimination (MED) is a general framework for discriminative estimation based on the maximum entropy and large margin principles, but it just uses only one view of the data not all the views of the data. Although Multi-view maximum entropy discrimination considered the multi-view information of the data, it just respects the consensus principle. There are two common principles named consensus and complementarity in multi-view learning. We aim to take the full advantage of the multi-view information of the data for classification, and respect the two common principles consensus and complementarity simultaneously. In this paper, we propose a new method called consensus and complementarity based MED (MED-2C) for multi-view classification, which well utilizes the two principles consensus and complementarity for multi-view learning (MVL). We first transform data from two views into a common subspace and make the transformed data in the new subspace identical to respect the consensus principle. Then we augment the transformed data with their original features to take into account the complementarity principle. Built on the augmented features and meanwhile by relaxing the consensus principle, the objective function of MED-2C is naturally formed whose inner optimization recovers the traditional MED framework. We provide an instantiation of the MED-2C method and derive the corresponding solution. Experimental results on synthetic data and real-world data show the effectiveness of the proposed MED-2C. It not only performs better than three single-view classification methods but also generally outperforms three multi-view classification methods canonical correlation analysis (CCA), ensemble MED (EMED) and the multi-view variant SVM-2K of the classical support vector machine (SVM) algorithm. In addition, MED-2C performs better than or as well as state-of-the-art MVMED on all the data sets.

Multi-attribute and relational learning via hypergraph regularized generative model

Abstract The real-world networking data may contain different types of attribute views and relational view. Hence, it is desirable to collectively use available attribute views and relational view in order to build effective learning models. We call this framework multi-attribute and relational learning. Collective classification is one of the popular approaches that can handle both attribute and relational information for network data. However, in collective classification only one type of attribute and relational view is involved and little attention is received for multi-attribute and relational learning. In this paper, we propose a new semi-supervised collective classification approach, called hypergraph regularized generative model (HRGM), for multi-attribute and relational learning. In the approach, a generative model based on the Probabilistic Latent Semantic Analysis (PLSA) method is developed to leverage attribute information, and a hypergraph regularizer is incorporated to effectively exploit higher-order relational information among the data samples. Experimental results on various data sets have demonstrated the effectiveness of the proposed HRGM, and revealed that our approach outperforms existing collective classification methods and multi-view classification methods in terms of accuracy.

Semi-supervised Collective Classification in Multi-attribute Network Data

Multi-attribute network refers to network data with multiple attribute views and relational view. Although semi-supervised collective classification has been investigated extensively, little attention is received for such kind of network data. In this paper, we aim to study and solve the semi-supervised learning problem for multi-attribute networks. There are two important challenges: (1) how to extract effective information from the rich multi-attribute and relational information; (2) how to make use of unlabeled data in the network. We propose a new generative model with network regularization, called MARL, which addresses the two challenges. In the approach, a generative model based on the probabilistic latent semantic analysis method is developed to leverage attribute information, and a network regularizer is incorporated to smooth label probability with relational information and unlabeled data. Comprehensive experiments on various data sets have been conducted to demonstrate the effectiveness of the proposed MARL, and the results reveal that our approach outperforms existing collective classification methods and multi-view classification methods in terms of accuracy.

The Total Least Squares Problem: Computational Aspects and Analysis (S. Van Huffel and J. Vandewalle)

Previous article Next article The Total Least Squares Problem: Computational Aspects and Analysis (S. Van Huffel and J. Vandewalle)Ricardo D. FierroRicardo D. Fierrohttps://doi.org/10.1137/1035157PDFBibTexSections ToolsAdd to favoritesExport CitationTrack CitationsEmail SectionsAbout[1] Ricardo D. Fierro and , James R. Bunch, Perturbation Theory for Orthogonal Projection Methods with Applications to Least Squares and Total Least Squares, CAM Report, 92-44, Dept. of Mathematics, Univ. of California, Los Angeles, 1992 Google Scholar[2] Leon Jay Gleser, Estimation in a multivariate “errors in variables” regression model: large sample results, Ann. Statist., 9 (1981), 24–44 82c:62096 0496.62049 CrossrefISIGoogle Scholar[3] Gene H. Golub and , Charles F. Van Loan, An analysis of the total least squares problem, SIAM J. Numer. Anal., 17 (1980), 883–893 10.1137/0717073 83g:65020 0468.65011 LinkISIGoogle Scholar Previous article Next article FiguresRelatedReferencesCited ByDetails New Multi-View Classification Method with Uncertain DataACM Transactions on Knowledge Discovery from Data, Vol. 16, No. 1 | 28 Feb 2022 Cross Ref An Iterative Approach for Accurate Dynamic Model Identification of Industrial RobotsIEEE Transactions on Robotics, Vol. 36, No. 5 | 1 Oct 2020 Cross Ref Analytical performance assessment of 1-D Structured Least Squares2011 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) | 1 May 2011 Cross Ref Arbitrarily colored ball detection using the structure tensor techniqueMechatronics, Vol. 21, No. 2 | 1 Mar 2011 Cross Ref Integration of frequency and space for multiple motion estimation and shape-independent object segmentationIEEE Transactions on Circuits and Systems for Video Technology, Vol. 18, No. 5 | 1 May 2008 Cross Ref Volume 35, Issue 4| 1993SIAM Review551-694 History Published online:12 July 2006 InformationCopyright © 1993 Society for Industrial and Applied MathematicsPDF Download Article & Publication DataArticle DOI:10.1137/1035157Article page range:pp. 660-662ISSN (print):0036-1445ISSN (online):1095-7200Publisher:Society for Industrial and Applied Mathematics