Robust Flexible Preserving Embedding

Influence Maximization in Multiagent Systems by a Graph Embedding Method: Dealing With Probabilistically Unstable Links

This article is concerned with the influence maximization (IM) problem under a network with probabilistically unstable links (PULs) via graph embedding for multiagent systems (MASs). First, two diffusion models, the unstable-link independent cascade (UIC) model and the unstable-link linear threshold (ULT) model, are designed for the IM problem under the network with PULs. Second, the MAS model for the IM problem with PULs is established and a series of interaction rules among agents are built for the MAS model. Third, the similarity of the unstable structure of the nodes is defined and a novel graph embedding method, termed the unstable-similarity2vec (US2vec) approach, is proposed to tackle the IM problem under the network with PULs. According to the embedding results of the US2vec approach, the seed set is figured out by the developed algorithm. Finally, extensive experiments are conducted to: 1) verify the validity of the proposed model and the developed algorithms and 2) illustrate the optimal solution for IM under different scenarios with PULs.

Regularization on Augmented Data to Diversify Sparse Representation for Robust Image Classification

Image classification is a fundamental component in modern computer vision systems, where sparse representation-based classification has drawn a lot of attention due to its robustness. However, on the optimization of sparse learning systems, regularization and data augmentation are both powerful, but currently isolated. We believe that regularization and data augmentation can cooperate to generate a breakthrough in robust image classification. In this article, we propose a novel framework, regularization on augmented data (READ), which creates diversification in the data using the generic augmentation techniques to implement robust sparse representation-based image classification. When the training data are augmented, READ applies a distinct regularizer, l₁ or l₂ , in particular, on the augmented training data apart from the original data, so that regularization and data augmentation are utilized and enhanced synchronously. We introduce an elaborate theoretical analysis on how to optimize the sparse representation by both l₁ -norm and l₂ -norm with the generic data augmentation and demonstrate its performance in extensive experiments. The results obtained on several facial and object datasets show that READ outperforms many state-of-the-art methods when using deep features.

A clustering-based adaptive Neighborhood Retrieval Visualizer

We introduce a novel adaptive version of the Neighborhood Retrieval Visualizer (NeRV). We maintain the advantages of the conventional NeRV method, while proposing an improvement of the data samples' neighborhood width calculation, in the input and output data space. In the standard NeRV, the data samples' neighborhood widths are determined in an arbitrary manner, in this way, inhibiting the possible quality of the resulting data visualization. We propose to compute the widths adaptively, on the basis of the input data scattering. Therefore, we first perform the preliminary input data clustering, next, we calculate the values of the inner-cluster variances, which convey the information on the input data scattering, then, we assign them to each data sample, and finally, we use them as the basis for the data samples' neighborhood widths determination. The results of the experiments conducted on the three different real datasets confirm the effectiveness and usefulness of the proposed approach.