Label distribution learning (LDL) is a novel multi-label learning paradigm proposed in recent years for solving label ambiguity. Existing approaches typically exploit label correlations globally to improve the effectiveness of label distribution learning, by assuming that the label correlations are shared by all instances. However, different instances may share different label correlations, and few correlations are globally applicable in real-world applications. In this paper, we propose a new label distribution learning algorithm by exploiting sample correlations locally (LDL-SCL). To encode the influence of local samples, we design a local correlation vector for each instance based on the clustered local samples. Then we predict the label distribution for an unseen instance based on the original features and the local correlation vector simultaneously. Experimental results demonstrate that LDL-SCL can effectively deal with the label distribution problems and perform remarkably better than the state-of-the-art LDL methods.

Open-source indicators such as social media can be very effective precursors for forecasting future societal events. As events are often preceded by social indicators generated by groups of people speaking many different languages, multiple languages need to be considered to ensure comprehensive event forecasting. However, this leads to several technical challenges for traditional models: 1) high dimension, sparsity, and redundancy of features; 2) translation correlation among the multilingual features. and 3) lack of language-wise supervision. In order to simultaneously address these issues, we present a novel model capable of distant-supervision of heterogeneous multitask learning (DHML) for multilingual spatial social event forecasting. This model maps the multilingual heterogeneous features into several latent semantic spaces and then enforces a similar sparsity pattern across them all, using distant supervision across all the languages involved. Optimizing this model creates a difficult problem that is nonconvex and nonsmooth that can then be decomposed into simpler subproblems using the Alternative Direction Multiplier of Methods (ADMM). A novel dynamic programming-based algorithm is proposed to solve one challenging subproblem efficiently. Theoretical properties of the proposed algorithm are analyzed. The results of extensive experiments on multiple real-world datasets are presented to demonstrate the effectiveness, efficiency, and interpretability of the proposed approach.

Earth Mover's Distance (EMD), targeting at measuring the many-to-many distances, has shown its superiority and been widely applied in computer vision tasks, such as object recognition, hyperspectral image classification and gesture recognition. However, there is still little effort concentrated on optimizing the EMD metric towards better matching performance. To tackle this issue, we propose an EMD metric learning algorithm in this paper. In our method, the objective is to learn a discriminative distance metric for EMD ground distance matrix generation which can better measure the similarity between compared subjects. More specifically, given a group of labeled data from different categories, we first select a subset of training data and then optimize the metric for ground distance matrix generation. Here, both the EMD metric and the EMD flow-network are alternatively optimized until a steady EMD value can be achieved. This method is able to generate a discriminative ground distance matrix which can further improve the EMD distance measurement. We then apply our EMD metric learning method on two tasks, i.e., multi-view object classification and document classification. The experimental results have shown better performance of our proposed EMD metric learning method compared with the traditional EMD method and the state-of-the-art methods. It is noted that the proposed EMD metric learning method can be also used in other applications.

Maximum margin clustering (MMC), which borrows the large margin heuristic from support vector machine (SVM), has achieved more accurate results than traditional clustering methods. The intuition is that, for a good clustering, when labels are assigned to different clusters, SVM can achieve a large minimum margin on this data. Recent studies, however, disclosed that maximizing the minimum margin does not necessarily lead to better performance, and instead, it is crucial to optimize the margin distribution. In this paper, we propose a novel approach ODMC (Optimal margin Distribution Machine for Clustering), which tries to cluster the data and achieve optimal margin distribution simultaneously. Specifically, we characterize the margin distribution by the first- and second-order statistics, i.e., the margin mean and variance, and extend a stochastic mirror descent method to solve the resultant minimax problem. Moreover, we prove theoretically that ODMC has the same convergence rate with state-of-the-art cutting plane based algorithms but involves much less computation cost per iteration, so our method is much more scalable than existing approaches. Extensive experiments on UCI data sets show that ODMC is significantly better than compared methods, which verifies the superiority of optimal margin distribution learning.

In multi-label learning, each training example is represented by a single instance (feature vector) while associated with multiple class labels simultaneously. The task is to learn a predictive model from the training examples which can assign a set of proper labels for the unseen instance. Most existing approaches make use of multi-label training examples by exploiting their labeling information in a crisp manner, i.e. one class label is either fully relevant or irrelevant to the instance. In this paper, a novel multi-label learning approach is proposed which aims to enrich the labeling information by leveraging the structural information in feature space. Firstly, the underlying structure of feature space is characterized by conducting sparse reconstruction among the training examples. Secondly, the reconstruction information is conveyed from feature space to label space so as to enrich the original categorical labels into numerical ones. Thirdly, the multi-label predictive model is induced by learning from training examples with enriched labeling information. Extensive experiments on fifteen benchmark data sets clearly validate the effectiveness of the proposed feature-induced strategy for enhancing labeling information of multi-label examples.

Understanding and predicting latent emotions of users toward online contents, known as social emotion mining, has become increasingly important to both social platforms and businesses alike. Despite recent developments, however, very little attention has been made to the issues of nuance, subjectivity, and bias of social emotions. In this paper, we fill this gap by formulating social emotion mining as a robust label ranking problem, and propose: (1) a robust measure, named as G-mean-rank (GMR), which sets a formal criterion consistent with practical intuition; and (2) a simple yet effective label ranking model, named as ROAR, that is more robust toward unbalanced datasets (which are common). Through comprehensive empirical validation using 4 real datasets and 16 benchmark semi-synthetic label ranking datasets, and a case study, we demonstrate the superiorities of our proposals over 2 popular label ranking measures and 6 competing label ranking algorithms. The datasets and implementations used in the empirical validation are available for access.

For real-world applications, data are often associated with multiple labels and represented with multiple views. Most existing multi-label learning methods do not sufficiently consider the complementary information among multiple views, leading to unsatisfying performance. To address this issue, we propose a novel approach for multi-view multi-label learning based on matrix factorization to exploit complementarity among different views. Specifically, under the assumption that there exists a common representation across different views, the uncovered latent patterns are enforced to be aligned across different views in kernel spaces. In this way, the latent semantic patterns underlying in data could be well uncovered and this enhances the reasonability of the common representation of multiple views. As a result, the consensus multi-view representation is obtained which encodes the complementarity and consistence of different views in latent semantic space. We provide theoretical guarantee for the strict convexity for our method by properly setting parameters. Empirical evidence shows the clear advantages of our method over the state-of-the-art ones.

The clustering methods have absorbed even-increasing attention in machine learning and computer vision communities in recent years. Exploring manifold information in multi-way graph cut clustering, such as ratio cut clustering, has shown its promising performance. However, traditional multi-way ratio cut clustering method is NP-hard and thus the spectral solution may deviate from the optimal one. In this paper, we propose a new relaxed multi-way graph cut clustering method, where l 2,1 -norm distance instead of squared distance is utilized to preserve the solution having much more clearer cluster structures. Furthermore, the resulting solution is constrained with normalization to obtain more sparse representation, which can encourage the solution to contain more discrete values with many zeros. For the objective function, it is very difficult to optimize due to minimizing the ratio of two non-smooth items. To address this problem, we transform the objective function into a quadratic problem on the Stiefel manifold (QPSM), and introduce a novel yet efficient iterative algorithm to solve it. Experimental results on several benchmark datasets show that our method significantly outperforms several state-of-the-art clustering approaches.

This paper addresses both the model selection (i.e., estimating the number of clusters K) and subspace clustering problems in a unified model. The real data always distribute on a union of low-dimensional sub-manifolds which are embedded in a high-dimensional ambient space. In this regard, the state-of-the-art subspace clustering approaches firstly learn the affinity among samples, followed by a spectral clustering to generate the segmentation. However, arguably, the intrinsic geometrical structures among samples are rarely considered in the optimization process. In this paper, we propose to simultaneously estimate K and segment the samples according to the local similarity relationships derived from the affinity matrix. Given the correlations among samples, we define a novel data structure termed the Triplet, each of which reflects a high relevance and locality among three samples which are aimed to be segmented into the same subspace. While the traditional pairwise distance can be close between inter-cluster samples lying on the intersection of two subspaces, the wrong assignments can be avoided by the hyper-correlation derived from the proposed triplets due to the complementarity of multiple constraints. Sequentially, we propose to greedily optimize a new model selection reward to estimate K according to the correlations between inter-cluster triplets. We simultaneously optimize a fusion reward based on the similarities between triplets and clusters to generate the final segmentation. Extensive experiments on the benchmark datasets demonstrate the effectiveness and robustness of the proposed approach.

Dictionary learning has been widely used in machine learning field to address many real-world applications, such as classification and denoising. In recent years, many new dictionary learning methods have been proposed. Most of them are designed to solve unsupervised problem without any prior information or supervised problem with the label information. But in real world, as usual, we can only obtain limited side information as prior information rather than label information. The existing methods don’t take into account the side information, let alone learning a good dictionary through using the side information. To tackle it, we propose a new unified unsupervised model which naturally integrates metric learning to enhance dictionary learning model with fully utilizing the side information. The proposed method updates metric space and dictionary adaptively and alternatively, which ensures learning optimal metric space and dictionary simultaneously. Besides, our method can also deal well with highdimensional data. Extensive experiments show the efficiency of our proposed method, and a better performance can be derived in real-world image clustering applications.