Transductive semi-supervised learning can only predict labels for unlabeled data appearing in training data, and can not predict labels for testing data never appearing in training set. To handle this out-of-sample problem, many inductive methods make a constraint such that the predicted label matrix should be exactly equal to a linear model. In practice, this constraint might be too rigid to capture the manifold structure of data. In this paper, we relax this rigid constraint and propose to use an elastic constraint on the predicted label matrix such that the manifold structure can be better explored. Moreover, since unlabeled data are often very abundant in practice and usually there are some outliers, we use a non-squared loss instead of the traditional squared loss to learn a robust model. The derived problem, although is convex, has so many nonsmooth terms, which make it very challenging to solve. In the paper, we propose an efficient optimization algorithm to solve a more general problem, based on which we find the optimal solution to the derived problem.

Multi-instance multi-label learning (MIML) is a learning paradigm where an object is represented by a bag of instances and each bag is associated with multiple labels. Ordinary MIML setting assumes a fixed target label set. In real applications, multiple novel labels may exist outside this set, but hidden in the training data and unknown to the MIML learner. Existing MIML approaches are unable to discover the hidden novel labels, let alone predicting these labels in the previously unseen test data. In this paper, we propose the first approach to discover multiple novel labels in MIML problem using an efficient augmented lagrangian optimization, which has a bag-dependent loss term and a bag-independent clustering regularization term, enabling the known labels and multiple novel labels to be modeled simultaneously. The effectiveness of the proposed approach is validated in experiments.

In this paper, we investigate how to automatically extract attractive summaries for news propagation on microblogs and propose a novel system called ATSUM to achieve this goal via text attractiveness analysis. It first analyzes the sentences in a news article and automatically predict the attractiveness score of each sentence by using the support vector regression method. The predicted attractiveness scores are then incorporated into a summarization system. Experimental results on a manually labeled dataset verify the effectiveness of the proposed methods.

Feature selection aims to select a small subset from the high-dimensional features which can lead to better learning performance, lower computational complexity, and better model readability. The class imbalance problem has been neglected by traditional feature selection methods, therefore the selected features will be biased towards the majority classes. Because of the superiority of F-measure to accuracy for imbalanced data, we propose to use F-measure as the performance measure for feature selection algorithms. As a pseudo-linear function, the optimization of F-measure can be achieved by minimizing the total costs. In this paper, we present a novel cost-sensitive feature selection (CSFS) method which optimizes F-measure instead of accuracy to take class imbalance issue into account. The features will be selected according to optimal F-measure classifier after solving a series of cost-sensitive feature selection sub-problems. The features selected by our method will fully represent the characteristics of not only majority classes, but also minority classes. Extensive experimental results conducted on synthetic, multi-class and multi-label datasets validate the efficiency and significance of our feature selection method.

Multi-View Clustering (MVC) has garnered more attention recently since many real-world data are comprised of different representations or views. The key is to explore complementary information to benefit the clustering problem. In this paper, we present a deep matrix factorization framework for MVC, where semi-nonnegative matrix factorization is adopted to learn the hierarchical semantics of multi-view data in a layer-wise fashion. To maximize the mutual information from each view, we enforce the non-negative representation of each view in the final layer to be the same. Furthermore, to respect the intrinsic geometric structure in each view data, graph regularizers are introduced to couple the output representation of deep structures. As a non-trivial contribution, we provide the solution based on alternating minimization strategy, followed by a theoretical proof of convergence. The superior experimental results on three face benchmarks show the effectiveness of the proposed deep matrix factorization model.

In this paper, we study the problem of user clustering in the context of their published short text streams. Clustering users by short text streams is more challenging than in the case of long documents associated with them as it is difficult to track users' dynamic interests in streaming sparse data. To obtain better user clustering performance, we propose a user collaborative interest tracking model (UCIT) that aims at tracking changes of each user's dynamic topic distributions in collaboration with their followees', based both on the content of current short texts and the previously estimated distributions. We evaluate our proposed method via a benchmark dataset consisting of Twitter users and their tweets. Experimental results validate the effectiveness of our proposed UCIT model that integrates both users' and their collaborative interests for user clustering by short text streams.

Privacy protection from surreptitious video recordings is an important societal challenge. We desire a computer vision system (e.g., a robot) that can recognize human activities and assist our daily life, yet ensure that it is not recording video that may invade our privacy. This paper presents a fundamental approach to address such contradicting objectives: human activity recognition while only using extreme low-resolution (e.g., 16x12) anonymized videos. We introduce the paradigm of inverse super resolution (ISR), the concept of learning the optimal set of image transformations to generate multiple low-resolution (LR) training videos from a single video. Our ISR learns different types of sub-pixel transformations optimized for the activity classification, allowing the classifier to best take advantage of existing high-resolution videos (e.g., YouTube videos) by creating multiple LR training videos tailored for the problem. We experimentally confirm that the paradigm of inverse super resolution is able to benefit activity recognition from extreme low-resolution videos.

Adverse drug-drug interactions (DDIs) remain a leading cause of morbidity and mortality around the world. Identifying potential DDIs during the drug design process is critical in guiding targeted clinical drug safety testing. Although detection of adverse DDIs is conducted during Phase IV clinical trials, there are still a large number of new DDIs founded by accidents after the drugs were put on market. With the arrival of big data era, more and more pharmaceutical research and development data are becoming available, which provides an invaluable resource for digging insights that can potentially be leveraged in early prediction of DDIs. Many computational approaches have been proposed in recent years for DDI prediction. However, most of them focused on binary prediction (with or without DDI), despite the fact that each DDI is associated with a different type. Predicting the actual DDI type will help us better understand the DDI mechanism and identify proper ways to prevent it. In this paper, we formulate the DDI type prediction problem as a multitask dyadic regression problem, where the prediction of each specific DDI type is treated as a task. Compared with conventional matrix completion approaches which can only impute the missing entries in the DDI matrix, our approach can directly regress those dyadic relationships (DDIs) and thus can be extend to new drugs more easily. We developed an effective proximal gradient method to solve the problem. Evaluation on real world datasets is presented to demonstrate the effectiveness of the proposed approach.

In order to grapple with the conundrum in the scalability of kernel-based learning algorithms, the method of approximating nonlinear kernels via random feature maps has attracted wide attention in large-scale learning systems. Specifically, the associated sampling procedure is one critical component that dictates the quality of random feature maps. However, for high-dimensional features, the standard Monte Carlo sampling method has been shown to be less effective in producing low-variance random samples. In consequence, it demands constructing a large number of features to attain the desired accuracy for downstream use. In this paper, we present a novel sampling algorithm powered by moment matching techniques to reduce the variance of random features. Our extensive empirical studies and comparisons with several highly competitive peer methods verify the superiority of the proposed algorithm in Gram matrix approximation and generalization errors in regression. Our rigorous theoretical proofs justify that the proposed algorithm is guaranteed achieving lower variance than the standard Monte Carlo method in high dimensional settings.

Error decomposition analysis is a key problem for ensemble learning. Two commonly used error decomposition schemes, the classic Ambiguity Decomposition and Bias-Variance-Covariance decomposition, are only suitable for regression tasks with square loss. We generalized the classic Ambiguity Decomposition from regression problems with square loss to classification problems with any loss functions that are twice differentiable, including the logistic loss in Logistic Regression, the exponential loss in Boosting methods, and the 0-1 loss in many other classification tasks. We further proved several important properties of the Ambiguity term, armed with which the Ambiguity terms of logistic loss, exponential loss and 0-1 loss can be explicitly computed and optimized. We further discussed the relationship between margin theory, "good'' and "bad'' diversity theory and our theoretical results, and provided some new insights for ensemble learning. We demonstrated the applications of our theoretical results in active learning and unsupervised ensemble pruning, and the experimental results confirmed the effectiveness of our methods.