Multi-view clustering aims to partition unlabeled patterns into disjoint clusters using consistent and complementary information derived from features of patterns in multiple views. Downstream methods perform this clustering sequentially: estimation of individual or consistent similarity matrices, spectral embedding, and clustering. In this article, we present an approach that can address some of the shortcomings of previous multiview clustering methods. We propose a single objective function whose optimization can jointly provide the consistent graph matrix for all views, the unified spectral data representation, the cluster assignments, and the view weights. We propose a new constraint term that sets the cluster index matrix to the convolution of the consistent spectral projection matrix over the consistent graph. Our proposed scheme has two interesting properties that the recent works do not have simultaneously. First, the cluster assignments can be estimated directly without the need for an additional clustering phase, which depends heavily on initialization. Second, the soft cluster assignments are directly linked to the kernel representation of the features of the views. Moreover, our method automatically computes the weights of each view, requiring fewer hyperparameters. We have conducted a series of experiments on real datasets. These demonstrate the effectiveness of the proposed approach, which compares favorably to many competing multi-view clustering methods.

Recently, graph-based semi-supervised learning (GSSL) has garnered significant interest in the realms of machine learning and pattern recognition. Although some of the proposed methods have made some progress, there are still some shortcomings that need to be overcome. There are three main limitations. First, the graphs used in these approaches are usually predefined regardless of the task at hand. Second, due to the use of graphs, almost all approaches are unable to process and consider data with a very large number of unlabeled samples. Thirdly, the imbalance of the topology of the samples is very often not taken into account. In particular, processing large datasets with GSSL might pose challenges in terms of computational resource feasibility. In this article, we present a scalable and inductive GSSL method. We broaden the scope of the graph topology imbalance paradigm to extensive databases. Second, we employ the calculated weights of the labeled sample for the label-matching term in the global objective function. This leads to a unified, scalable, semi-supervised learning model that allows simultaneous labeling of unlabeled data, projection of the feature space onto the labeling space, along with the graph matrix of anchors. In the proposed scheme, the integration of labels and features from anchors is applied for the adaptive construction of the anchor graph. Experimental results were performed on four large databases: NORB, RCV1, Covtype, and MNIST. These experiments demonstrate that the proposed method exhibits superior performance when compared to existing scalable semi-supervised learning models.

Recently, structured computing has become an interesting topic in the world of artificial intelligence, especially in the field of machine learning, as most researchers focus on the development of graph-based semi-supervised learning models. In this article, we present a new framework for graph-based semi-supervised learning. We present a powerful method for simultaneous label inference and linear transform estimation. The targeted linear transformation is used to obtain a discriminant subspace. To improve semi-supervised learning, our framework focuses on exploiting the data structure and soft labels of the available unlabeled samples. In the iterative optimization scheme used, the prior estimation of the label increases the supervision information indirectly through an introduced informative matrix called the label graph, thus avoiding the use of hard confidence-based decisions as used in self-supervised methods. In addition, the estimation of labels and projected data is made more robust by using smoothing concepts based on hybrid graphs. For each type of smoothing, the hybrid graph is an adaptive fusion of the two graphs encoding the similarity of the data and the similarity of the labels. The proposed method leads to an improved discriminative linear transformation. Several experimental results on real image datasets confirm the effectiveness of the proposed method. They also show superior performance compared to semi-supervised methods that use integration and label inference simultaneously.

Graph Convolutional Networks (GCNs) have received a lot of attention in pattern recognition and machine learning. In this paper, we present a revisited scheme for the new method called ”GCNs with Manifold Regularization” (GCNMR). While manifold regularization can add additional information, the GCN-based semi-supervised classification process cannot consider the full layer-wise structured information. Inspired by graph-based label propagation approaches, we will integrate high-order feature propagation into each GCN layer. High-order feature propagation over the graph can fully exploit the structured information provided by the latter at all the GCN’s layers. It fully exploits the clustering assumption, which is valid for structured data but not well exploited in GCNs. Our proposed scheme would lead to more informative GCNs. Using the revisited model, we will conduct several semi-supervised classification experiments on public image datasets containing objects, faces and digits: Extended Yale, PF01, Caltech101 and MNIST. We will also consider three citation networks. The proposed scheme performs well compared to several semi-supervised methods. With respect to the recent GCNMR approach, the average improvements were 2.2%, 4.5%, 1.0% and 10.6% on Extended Yale, PF01, Caltech101 and MNIST, respectively.

Recently, the estimation of facial age has attracted much attention. This letter extends and improves a recently developed method (Guehairia et al., 2020) for fusing multiple deep facial features for age estimation. This method was based on deep random forests. We propose a new pipeline that integrates tensor-based subspace learning before applying DRFs. Deep face features of a training set are represented as a 3D tensor. Multi-linear Whitened Principal Component (MWPCA) and Tensor Exponential Discriminant (TEDA) are used to extract the most discriminative information. The tensor subspace features are then fed into DRFs to predict age. Experiments conducted on five public face databases show that our method can compete with many state-of-the-art methods.

Recently, one-step clustering methods have shown good performance. However, very few one-step approaches have addressed the multi-view case, where an instance may have multiple representations. Data can be represented with multiple heterogeneous views. Clustering with multiple views faces the challenge of how to combine all the different views. A general scheme is to represent the views by view-based graphs and/or a consensus graph. Graphs can be well suited for clustering problems since they can capture the local and global structure of the data. In this paper, we present a novel approach to one-step graph-based multi-view clustering. In contrast to existing graph-based one-step clustering methods, our proposed method introduces two key innovations. First, we build an additional graph by using the cluster label correlation to the graphs associated with the data space. Second, a smoothing constraint is exploited to constrain the cluster-label matrix and make it more consistent with the original data graphs as well as with and label graphs. Experimental results on several public datasets show the efficiency of the proposed approach. All cluster evaluation metrics show significant improvement by applying our method to different types and sizes of datasets. The average improvement (across all datasets) is the difference between the indicator obtained by our approach and the indicator obtained by the most competitive method. The average improvement is approximately 4%, 2%, 3%, and 2% for the Accuracy indicator, the Normalized Mutual Information indicator, the Purity indicator, and the Adjusted Rand index, respectively.

This article introduces a scheme for semi-supervised learning by estimating a flexible non-linear data representation that exploits Spectral Graph Convolutions structure. Structured data are exploited in order to determine non-linear and linear models. The introduced scheme takes advantage of data-driven graphs at two levels. First, it incorporates manifold smoothness that is naturally encoded by the graph itself. Second, the regression model is built on the convolved data samples that are derived from the data and their associated graph. The proposed semi-supervised embedding can tackle the problem of over-fitting on neighborhood structures for image data. The proposed Graph Convolution-based Semi-supervised Embedding paves the way to new theoretical and application perspectives related to the non-linear embedding. Indeed, building flexible models that adopt convolved data samples can enhance both the data representation and the final performance of the learning system. Several experiments are conducted on six image datasets for comparing the introduced scheme with some state-of-the-art semi-supervised approaches. This empirical evaluation shows the effectiveness of the proposed embedding scheme.

The Sparse Representation Classifier, the Collaborative Representation Classifier (CRC), and the Two Phase Test Sample Sparse Representation (TPTSSR) classifier were introduced in recent times. All these frameworks are supervised and passive in the sense that they cannot benefit from unlabeled data samples. In this paper, inspired by active learning paradigms, we introduce an active CRC that can be used by these frameworks. More precisely, we are interested in the TPTSSR framework due to its good performance and its reasonable computational cost. Our proposed Active Two Phase Collaborative Representation Classifier (ATPCRC) starts by predicting the label of the available unlabeled samples. At testing stage, two coding processes are carried out separately on the set of originally labeled samples and the whole set (original and predicted label). The two types of class-wise reconstruction errors are blended in order to decide the class of any test image. Experiments conducted on four public image datasets show that the proposed ATPCRC can outperform the classic TPTSSR as well as many state-of-the-art methods that exploit label and unlabeled data samples.