Structure learning is a fundamental and challenging issue in dealing with Bayesian networks. In this paper we introduce a two-step clustering-based strategy, which can automatically generate prior information from data in order to further improve the accuracy and time efficiency of state-of-the-art algorithms in Bayesian network structure learning. Our clustering-based strategy is composed of two steps. In the first step, we divide the potential nodes into several groups via clustering analysis and apply Bayesian network structure learning to obtain some pre-existing arcs within each cluster. In the second step, with all the within-cluster arcs being well preserved, we learn the between-cluster structure of the given network. Experimental results on benchmark data sets show that a wide range of structure learning algorithms benefit from the proposed clustering-based strategy in terms of both accuracy and efficiency.

Preserving differential privacy during empirical risk minimization model training has been extensively studied under centralized and sample-wise distributed dataset settings. This paper considers a nearly unexplored context with features partitioned among different parties under privacy restriction. Motivated by the nearly optimal utility guarantee achieved by centralized private Frank-Wolfe algorithm (Talwar, Thakurta, and Zhang 2015), we develop a distributed variant with guaranteed privacy, utility and uplink communication complexity. To obtain these guarantees, we provide a much generalized convergence analysis for block-coordinate Frank-Wolfe under arbitrary sampling, which greatly extends known convergence results that are only applicable to two specific block sampling distributions. We also design an active feature sharing scheme by utilizing private Johnson-Lindenstrauss transform, which is the key to updating local partial gradients in a differentially private and communication efficient manner.

Tracking target of interests is an important step for motion perception in intelligent video surveillance systems. While most recently developed tracking algorithms are grounded in RGB image sequences, it should be noted that information from RGB modality is not always reliable (e.g. in a dark environment with poor lighting condition), which urges the need to integrate information from infrared modality for effective tracking because of the insensitivity to illumination condition of infrared thermal camera. However, several issues encountered during the tracking process limit the fusing performance of these heterogeneous modalities: 1) the cross-modality discrepancy of visual and motion characteristics, 2) the uncertainty of degree of reliability in different modalities, and 3) large target appearance variations and background distractions within each modality. To address these issues, this paper proposes a novel and optimal discriminative learning framework for multi-modality tracking. In particular, the proposed discriminative learning framework is able to: 1) jointly eliminate outlier samples caused by large variations and learn discriminability-consistent features from heterogeneous modalities, and 2) collaboratively perform modality reliability measurement and target-background separation. Extensive experiments on RGB-infrared image sequences demonstrate the effectiveness of the proposed method.

Unsupervised domain adaptation has been proved to be a promising approach to solve the problem of dataset bias. To employ source labels in the target domain, it is required to align the joint distributions of source and target data. To do this, the key research problem is to align conditional distributions across domains without target labels. In this paper, we propose a new criterion of domain-shared group-sparsity that is an equivalent condition for conditional distribution alignment. To solve the problem in joint distribution alignment, a domain-shared group-sparse dictionary learning method is developed towards joint alignment of conditional and marginal distributions. A classifier for target domain is trained using the domain-shared group-sparse coefficients and the target-specific information from the target data. Experimental results on cross-domain face and object recognition show that the proposed method outperforms eight state-of-the-art unsupervised domain adaptation algorithms.

Predicting epidemic dynamics is of great value in understanding and controlling diffusion processes, such as infectious disease spread and information propagation. This task is intractable, especially when surveillance resources are very limited. To address the challenge, we study the problem of active surveillance, i.e., how to identify a small portion of system components as sentinels to effect monitoring, such that the epidemic dynamics of an entire system can be readily predicted from the partial data collected by such sentinels. We propose a novel measure, the gamma value, to identify the sentinels by modeling a sentinel network with row sparsity structure. We design a flexible group sparse Bayesian learning algorithm to mine the sentinel network suitable for handling both linear and non-linear dynamical systems by using the expectation maximization method and variational approximation. The efficacy of the proposed algorithm is theoretically analyzed and empirically validated using both synthetic and real-world data.

Person re-identification is widely studied in visible spectrum, where all the person images are captured by visible cameras. However, visible cameras may not capture valid appearance information under poor illumination conditions, e.g, at night. In this case, thermal camera is superior since it is less dependent on the lighting by using infrared light to capture the human body. To this end, this paper investigates a cross-modal re-identification problem, namely visible-thermal person re-identification (VT-REID). Existing cross-modal matching methods mainly focus on modeling the cross-modality discrepancy, while VT-REID also suffers from cross-view variations caused by different camera views. Therefore, we propose a hierarchical cross-modality matching model by jointly optimizing the modality-specific and modality-shared metrics. The modality-specific metrics transform two heterogenous modalities into a consistent space that modality-shared metric can be subsequently learnt. Meanwhile, the modality-specific metric compacts features of the same person within each modality to handle the large intra-modality intra-person variations (e.g. viewpoints, pose). Additionally, an improved two-stream CNN network is presented to learn the multi-modality sharable feature representations. Identity loss and contrastive loss are integrated to enhance the discriminability and modality-invariance with partially shared layer parameters. Extensive experiments illustrate the effectiveness and robustness of the proposed method.

In this paper we introduce a two-step clustering-based strategy, which can automatically generate prior information from data in order to further improve the accuracy and time efficiency of state-of-the-art algorithms for Bayesian network structure learning. Our clustering-based strategy is composed of two steps. In the first step, we divide the potential nodes into several groups via clustering analysis and apply Bayesian network structure learning to obtain some pre-existing arcs within each cluster. In the second step, with all the within-cluster arcs being well preserved, we learn the between-cluster structure of the given network. Experimental results on benchmark datasets show that a wide range of structure learning algorithms benefit from the proposed clustering-based strategy in terms of both accuracy and efficiency.

Community detection is a fundamental and widely-studied problem that finds all densely-connected groups of nodes and well separates them from others in graphs. With the proliferation of rich information available for entities in real-world networks, it is useful to discover communities in attributed graphs where nodes tend to have attributes. However, most existing attributed community detection methods directly utilize the original network topology leading to poor results due to ignoring inherent community structures. In this paper, we propose a novel embedding based model to discover communities in attributed graphs. Specifically, based on the observation of densely-connected structures in communities, we develop a novel community structure embedding method to encode inherent community structures via underlying community memberships. Based on node attributes and community structure embedding, we formulate the attributed community detection as a nonnegative matrix factorization optimization problem. Moreover, we carefully design iterative updating rules to make sure of finding a converging solution. Extensive experiments conducted on 19 attributed graph datasets with overlapping and non-overlapping ground-truth communities show that our proposed model CDE can accurately identify attributed communities and significantly outperform 7 state-of-the-art methods.

Canonical Correlation Analysis (CCA) is a classical technique for two-view correlation analysis, while Probabilistic CCA (PCCA) provides a generative and more general viewpoint for this task. Recently, PCCA has been extended to bilinear cases for dealing with two-view matrices in order to preserve and exploit the matrix structures in PCCA. However, existing bilinear PCCAs impose restrictive model assumptions for matrix structure preservation, sacrificing generative correctness or model flexibility. To overcome these drawbacks, we propose BPCCA, a new bilinear extension of PCCA, by introducing a hybrid joint model. Our new model preserves matrix structures indirectly via hybrid vector-based and matrix-based concatenations. This enables BPCCA to gain more model flexibility in capturing two-view correlations and obtain close-form solutions in parameter estimation. Experimental results on two real-world applications demonstrate the superior performance of BPCCA over competing methods.

Embedded feature selection is effective when both prediction and interpretation are needed. The Lasso and its extensions are standard methods for selecting a subset of features while optimizing a prediction function. In this paper, we are interested in embedded feature selection for multidimensional data, wherein (1) there is no need to reshape the multidimensional data into vectors and (2) structural information from multiple dimensions are taken into account. Our main contribution is a new method called Regularized multilinear regression and selection (Remurs) for automatically selecting a subset of features while optimizing prediction for multidimensional data. Both nuclear norm and the ℓ 1 -norm are carefully incorporated to derive a multi-block optimization algorithm with proved convergence. In particular, Remurs is motivated by fMRI analysis where the data are multidimensional and it is important to find the connections of raw brain voxels with functional activities. Experiments on synthetic and real data show the advantages of Remurs compared to Lasso, Elastic Net, and their multilinear extensions.