Absent Multiview Semisupervised Classification.

With the advent of vast data collection ways, data are often with multiple modalities or coming from multiple sources. Traditional multiview learning often assumes that each example of data appears in all views. However, this assumption is too strict in some real applications such as multisensor surveillance system, where every view suffers from some data absent.

Identifying highly influential nodes in multilayer networks based on global propagation.

Based on percolation theory and the independent cascade model, this paper considers the selection of the optimal propagation source when the propagation probability is greater than the percolation threshold. First, based on the percolation characteristics of real networks, this paper presents an iterative algorithm of linear complexity to solve the probability of the propagation source transmitting information to the network's giant component, that is, the global propagation probability. Compared with the previous multiple local simulation algorithm, this algorithm eliminates random errors and significantly reduces the operation time.

Joint Embedding Learning and Low-Rank Approximation: A Framework for Incomplete Multiview Learning.

In real-world applications, not all instances in the multiview data are fully represented. To deal with incomplete data, incomplete multiview learning (IML) rises. In this article, we propose the joint embedding learning and low-rank approximation (JELLA) framework for IML.

Community discovering in temporal network with spectral fusion.

With the deep understanding of the time-varying characteristics of real systems, research studies focusing on the temporal network spring up like mushrooms. Community detection is an accompanying and meaningful problem in the temporal network, but the analysis of this problem is still in its developing stage. In this paper, we proposed a temporal spectral clustering method to detect the invariable communities in the temporal network.

Robustness of partially interdependent networks under combined attack.

We thoroughly study the robustness of partially interdependent networks when suffering attack combinations of random, targeted, and localized attacks. We compare analytically and numerically the robustness of partially interdependent networks with a broad range of parameters including coupling strength, attack strength, and network type. We observe the first and second order phase transition and accurately characterize the critical points for each combined attack.

Multiview Classification With Cohesion and Diversity.

Different views of multiview data share certain common information (consensus) and also contain some complementary information (complementarity). Both consensus and complementarity are of significant importance to the success of multiview learning. In this paper, we explicitly formulate both of them for multiview classification.

Dimension Reduction for Non-Gaussian Data by Adaptive Discriminative Analysis.

High-dimensional non-Gaussian data are ubiquitous in many real applications. Face recognition is a typical example of such scenarios. The sampled face images of each person in the original data space are more closely located to each other than to those of the same individuals due to the changes of various conditions like illumination, pose variation, and facial expression.

Full Representation Data Embedding via Nonoverlapping Historical Features.

Data recycling, which reuses the historical data to assist the present data to achieve better performance, is an emerging and important research topic. A common case is that historical examples only have features from one source while presently have more data collection ways and extract different types of features simultaneously for new examples. Previous studies assume that either historical data appear in all sources, or at least there is one type of representations for all data.

Scalable Multi-View Semi-Supervised Classification via Adaptive Regression.

With the advent of multi-view data, multi-view learning has become an important research direction in machine learning and image processing. Considering the difficulty of obtaining labeled data in many machine learning applications, we focus on the multi-view semi-supervised classification problem. In this paper, we propose an algorithm named multi-view semi-supervised classification via adaptive regression (MVAR) to address this problem.

Robust auto-weighted multi-view subspace clustering with common subspace representation matrix.

In many computer vision and machine learning applications, the data sets distribute on certain low-dimensional subspaces. Subspace clustering is a powerful technology to find the underlying subspaces and cluster data points correctly. However, traditional subspace clustering methods can only be applied on data from one source, and how to extend these methods and enable the extensions to combine information from various data sources has become a hot area of research.

Low-Frequency Error Extraction and Compensation for Attitude Measurements from STECE Star Tracker.

The low frequency errors (LFE) of star trackers are the most penalizing errors for high-accuracy satellite attitude determination. Two test star trackers- have been mounted on the Space Technology Experiment and Climate Exploration (STECE) satellite, a small satellite mission developed by China. To extract and compensate the LFE of the attitude measurements for the two test star trackers, a new approach, called Fourier analysis, combined with the Vondrak filter method (FAVF) is proposed in this paper.

Maximizing the Spread of Influence via Generalized Degree Discount.

It is a crucial and fundamental issue to identify a small subset of influential spreaders that can control the spreading process in networks. In previous studies, a degree-based heuristic called DegreeDiscount has been shown to effectively identify multiple influential spreaders and has severed as a benchmark method. However, the basic assumption of DegreeDiscount is not adequate, because it treats all the nodes equally without any differences.

Effective Discriminative Feature Selection With Nontrivial Solution.

Feature selection and feature transformation, the two main ways to reduce dimensionality, are often presented separately. In this paper, a feature selection method is proposed by combining the popular transformation-based dimensionality reduction method linear discriminant analysis (LDA) and sparsity regularization. We impose row sparsity on the transformation matrix of LDA through l2,1-norm regularization to achieve feature selection, and the resultant formulation optimizes for selecting the most discriminative features and removing the redundant ones simultaneously.

Modeling the citation network by network cosmology.

Citation between papers can be treated as a causal relationship. In addition, some citation networks have a number of similarities to the causal networks in network cosmology, e.g.

Discriminative embedded clustering: a framework for grouping high-dimensional data.

In many real applications of machine learning and data mining, we are often confronted with high-dimensional data. How to cluster high-dimensional data is still a challenging problem due to the curse of dimensionality. In this paper, we try to address this problem using joint dimensionality reduction and clustering.

Joint embedding learning and sparse regression: a framework for unsupervised feature selection.

Feature selection has aroused considerable research interests during the last few decades. Traditional learning-based feature selection methods separate embedding learning and feature ranking. In this paper, we propose a novel unsupervised feature selection framework, termed as the joint embedding learning and sparse regression (JELSR), in which the embedding learning and sparse regression are jointly performed.

Efficient image classification via multiple rank regression.

The problem of image classification has aroused considerable research interest in the field of image processing. Traditional methods often convert an image to a vector and then use a vector-based classifier. In this paper, a novel multiple rank regression model (MRR) for matrix data classification is proposed.

Listen to genes: dealing with microarray data in the frequency domain.

Background: We present a novel and systematic approach to analyze temporal microarray data. The approach includes normalization, clustering and network analysis of genes.

Methodology: Genes are normalized using an error model based uniform normalization method aimed at identifying and estimating the sources of variations.