PU Matrix Completion Based Multi-label Classification with Missing Labels

In multi-label classification, each example can be associated with multiple labels simultaneously. The task of learning from multilabel data can be addressed by methods that transform the multi-label classification problem into several single-label classification problems. The binary relevance approach is one of these methods, where the multilabel learning task is decomposed into several independent binary classification problems, one for each label in the set of labels, and the final labels for each example are determined by aggregating the predictions from all binary classifiers. However, this approach fails to consider any dependency among the labels. In this paper, we consider a simple approach which can be used to explore labels dependency aiming to accurately predict label combinations. An experimental study using decision trees, a kernel method as well as Naive Bayes as base-learning techniques shows the potential of the proposed approach to improve the multi-label classification performance.

The idea to exploit label dependencies for better prediction is at the core of methods for multi-label classification (MLC), and performance improvements are normally explained in this way. Surprisingly, however, there is no established methodology that allows to analyze the dependence-awareness of MLC algorithms. With that goal in mind, we introduce a class of loss functions that are able to capture the important aspect of label dependence. To this end, we leverage the mathematical framework of non-additive measures and integrals. Roughly speaking, a non-additive measure allows for modeling the importance of correct predictions of label subsets (instead of single labels), and thereby their impact on the overall evaluation, in a flexible way. The well-known Hamming and subset 0/1 losses are rather extreme special cases of this function class, which give full importance to single label sets or the entire label set, respectively. We present concrete instantiations of this class, which appear to be especially appealing from a modeling perspective. The assessment of multi-label classifiers in terms of these losses is illustrated in an empirical study, clearly showing their aptness at capturing label dependencies. Finally, while not being the main goal of this study, we also show some preliminary results on the minimization of this parametrized family of losses.

In this paper, we specifically examine the training of a multi-label classifier from data with incompletely assigned labels. This problem is fundamentally important in many multi-label applications because it is almost impossible for human annotators to assign a complete set of labels, although their judgments are reliable. In other words, a multilabel dataset usually has properties by which (1) assigned labels are definitely positive and (2) some labels are absent but are still considered positive. Such a setting has been studied as a positive and unlabeled (PU) classification problem in a binary setting. We treat incomplete label assignment problems as a multi-label PU ranking, which is an extension of classical binary PU problems to the wellstudied rank-based multi-label classification. We derive the conditions that should be satisfied to cancel the negative effects of label incompleteness. Our experimentally obtained results demonstrate the effectiveness of these conditions.

Multi-label classification (MLC) deals with the task of assigning an instance to all its relevant classes. This task becomes challenging in the presence of the label dependencies. The MLC methods that assume label independence do not use the dependencies among labels. We present a two-stage framework which improves the performance of MLC by using label dependencies. In the first stage, a standard MLC method is used to get the confidence scores for different labels. A conditional random field (CRF) is used in the second stage that improves the performance of the first-stage MLC by using the label dependencies among labels. An optimization-based framework is used to learn the structure and parameters of the CRF. Experiments show that the proposed model performs better than the state-of-the-art methods for MLC.

Using tensor products to detect unconditional label dependence in multilabel classifications

Machine learning involves the task of training systems to be able to make decisions without being explicitly programmed. Important among machine learning tasks is classification involving the process of training machines to make predictions from predefined labels. Classification is broadly categorized into three distinct groups: single-label (SL), multi-class, and multi-label (ML) classification. This research work presents an application of a multi-label classification (MLC) technique in automating Quranic verses labeling. MLC has been gaining attention in recent years. This is due to the increasing amount of works based on real-world classification problems of multi-label data. In traditional classification problems, patterns are associated with a single-label from a set of disjoint labels. However, in MLC, an instance of data is associated with a set of labels. In this paper, three standard <em>MLC</em> methods: <span>binary relevance (BR), classifier chain (CC), and label powerset (LP) algorithms are implemented with four baseline classifiers: support vector machine (SVM), naïve Bayes (NB), k-nearest neighbors (k-NN), and J48. The research methodology adopts the multi-label problem transformation (PT) approach. The results are validated using six conventional performance metrics. These include: hamming loss, accuracy, one error, micro-F1, macro-F1, and avg. precision. From the results, the classifiers effectively achieved above 70% accuracy mark. Overall, SVM achieved the best results with CC and LP algorithms.</span>

The rapid growth of unstructured textual data in various domains has necessitated the development of sophisticated techniques for multi-label text classification. Traditional methods often struggle with handling the complexity and interdependence of multiple labels, leading to suboptimal performance. This paper presents an advanced approach that integrates the Beta Ant Colony Optimization (BACO) algorithm with deep learning techniques for multi-label text classification. The BACO algorithm effectively explores the feature space and selects relevant features by leveraging pheromone trails, while the deep learning model captures intricate patterns and relationships within the textual data. The integration of these two methodologies aims to enhance the efficiency and accuracy of multi-label classification tasks, particularly in domains where label dependency is prominent. Empirical evaluations on benchmark datasets demonstrate that the proposed hybrid approach outperforms existing state-of-the-art techniques in terms of precision, recall, F1-score, and computational efficiency. The findings suggest that combining heuristic optimization algorithms with deep learning can significantly improve multi-label text classification performance, providing a robust solution for real-world applications.

Multi-label classification is a vital problem, as it has numerous applications in computer vision, such as automatic image annotation. The label set for each instance is always assumed to be in the original whole form. However, missing labels often occur because manual labelling is a time-consuming and label-intensive work in the case of large amount of data. The incompleteness of labels can certainly increase the difficulty of training the multi-label model. In this paper, a novel multi-label classification method is proposed that can learn the inductive classifier while explicitly dealing with missing labels. An individual sparsity inducing l1-norm is employed to capture the sparse label interdependencies. A group sparsity inducing l2,1-norm is utilized to select the discriminative input features. The semantic label hierarchy is included to diversify the label dependency. Meanwhile, the consistency between the predicted labels and the original labels as well as the regularization of smoothness on the predicted labels are also enforced to improve the classification performance. Furthermore, an efficient method based on the alternating direction method of multipliers is designed to facilitate classifier and label correlation learning process. Experiments on two widely used large-scale image datasets demonstrate that the efficacy of the proposed method on multi-label classification when only a limited number of labels are given for each training sample.

While deep convolutional neural networks (CNNs) have shown a great success in single-label image classification, it is important to note that real world images generally contain multiple labels, which could correspond to different objects, scenes, actions and attributes in an image. Traditional approaches to multi-label image classification learn independent classifiers for each category and employ ranking or thresholding on the classification results. These techniques, although working well, fail to explicitly exploit the label dependencies in an image. In this paper, we utilize recurrent neural networks (RNNs) to address this problem. Combined with CNNs, the proposed CNN-RNN framework learns a joint image-label embedding to characterize the semantic label dependency as well as the image-label relevance, and it can be trained end-to-end from scratch to integrate both information in a unified framework. Experimental results on public benchmark datasets demonstrate that the proposed architecture achieves better performance than the state-of-the-art multi-label classification model

Subspace clustering based on latent low rank representation with Frobenius norm minimization

Multi-view clustering utilizes information from diverse views to improve the performance of clustering. For most existing multi-view spectral clustering methods, information of different views is integrated by pursuing a consensus similarity matrix for clustering. However, view-specific structures, which contain the complementary information of multi-view data, may be lost during the clustering process. Actually, in multi-view spectral clustering, similarity matrices of multiple views would have the same clustering structures or properties rather than be numerically uniform. To overcome the aforementioned problem, a novel essential multi-view graph learning (EMGL) method for clustering is proposed in this paper. Different from most existing multi-view spectral clustering, an orthogonal matrix factorization is imposed on multi-view similarity matrices for making them have the same nuclear norm, which indicates the same clustering structures of different views. Furthermore, we also propose an alternating direction method of multipliers (ADMM) based optimization algorithm to address the objective function of our method. Extensive experiments on several datasets demonstrate the superior performance of our proposed method.

Moving object detection and its associated background-foreground separation have been widely used in a lot of applications, including computer vision, transportation and surveillance. Due to the presence of the static background, a video can be naturally decomposed into a low-rank background and a sparse foreground. Many regularization techniques, such as matrix nuclear norm, can therefore be imposed on the background. In the meanwhile, sparsity or smoothness based regularizations, such as total variation and $\ell_{1}$, can be imposed on the foreground. Moreover, graph Laplacians are further used to capture the complicated geometry of background images. Recently, weighted regularization techniques including the weighted nuclear norm regularization have been proposed in the image processing community to promote adaptive sparsity while achieving efficient performance. In this paper, we propose a robust dual-graph regularized moving object detection model based on a new weighted nuclear norm regularization and spatiotemporal graph Laplacians, which is solved by the alternating direction method of multipliers (ADMM). Numerical experiments on realistic body movement data sets have demonstrated the effectiveness of this method in separating moving objects from background, and the great potential in robotic applications.

As we know, nuclear norm based matrix regression (NMR) methods have the popular applications to face reconstruction and recognition with occlusion and illumination changes. However, when facing larger occlusions and heavier illuminations in real-world applications, these methods usually can not work well due to the biased estimator of nuclear norm as the rank relaxation. To overcome this issue, noconvex matrix regression approaches are presented by generalized nonconvex rank relaxations derived from several ℓ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0</sub> -norm substitutes, to characterize the low-rank structure of the residual image matrix. The representation coefficient vectors can be achieved with the help of nonconvex and even multi-variables alternating direction method of multipliers (ADMM) with theoretical analysis to optimize the developed problems and guarantee the closed-form solution of each subproblem, synchronously. Moreover, we design the minimal representation formula for the residual matrix as a decision rule. Finally, experimental results on face reconstruction and recognition can show the superiority of the proposed methodologies over the mostly related matrix regression methods including ℓ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> -norm and ℓ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> -norm regularized NMR methods.

Feature selection can remove non-important features from the data and promote better classifiers. This task, when applied to multi-label data where each instance is associated with a set of labels, supports emerging applications. Although multi-label data usually exhibit label relations, label dependence has been little studied in feature selection. We proposed two multi-label feature selection algorithms that consider label relations. These methods were experimentally competitive with traditional approaches. Moreover, this work conducted a systematic literature review, summarizing 74 related papers.

The world produces vast quantities of high-dimensional multi-semantic data. However, extracting valuable information from such a large amount of high-dimensional and multi-label data is undoubtedly arduous and challenging. Fe... | Find, read and cite all the research you need on Tech Science Press