Accurate and sufficient location information is the prerequisite for most Wireless Sensor Networks (WSNs) applications. Existing range-based localization approaches often suffer from incomplete and corrupted range measurements. Recently, some matrix completion based localization approaches have been proposed, which only take into account Gaussian noise and outlier noise when modeling the range measurements. However, in some real-world applications the inevitable structural noise usually degrades the localization accuracy and prevents the outlier recognition drastically. To address these challenges, we propose a noise-tolerant Localization via Multi-norms Regularized Matrix Completion (LMRMC) approach in this paper. Leveraging the intrinsic low-rank property of Euclidean Distance Matrix (EDM), the reconstruction problem of true underlying EDM is formulated as a Multi-norms Regularized Matrix Completion (MRMC) model, where the outlier noise and structural noise are explicitly sifted by L1-norm and L1,2-norm respectively, while the Gaussian noise is implicitly smoothed by employing the well-known Alternating Direction Method of Multiplier (ADMM) optimization method. To the best of our knowledge, this is the first scheme being able to efficiently recover the unknown range measurements under the coexistence of Gaussian noise, outlier noise and structural noise. Extensive experiments validate the superiority of our proposed LMRMC approach, outperforming the state-of-the-art localization approaches with regard to the localization accuracy. Besides, LMRMC can also achieve an accurate detection of both outlier noise and structural noise, making it promising for further nodes fault diagnosis and topology control in WSNs.

The resting-state functional magnetic resonance imaging (rs-fMRI) reflects functional activity of brain regions by blood-oxygen-level dependent (BOLD) signals. Up to now, many computer-aided diagnosis methods based on rs-fMRI have been developed for Autism Spectrum Disorder (ASD). These methods are mostly the binary classification approaches to determine whether a subject is an ASD patient or not. However, the disease often consists of several sub-categories, which are complex and thus still confusing to many automatic classification methods. Besides, existing methods usually focus on the functional connectivity (FC) features in grey matter regions, which only account for a small portion of the rs-fMRI data. Recently, the possibility to reveal the connectivity information in the white matter regions of rs-fMRI has drawn high attention. To this end, we propose to use the patch-based functional correlation tensor (PBFCT) features extracted from rs-fMRI in white matter, in addition to the traditional FC features from gray matter, to develop a novel multi-class ASD diagnosis method in this work. Our method has two stages. Specifically, in the first stage of multi-source domain adaptation (MSDA), the source subjects belonging to multiple clinical centers (thus called as source domains) are all transformed into the same target feature space. Thus each subject in the target domain can be linearly reconstructed by the transformed subjects. In the second stage of multi-view sparse representation (MVSR), a multi-view classifier for multi-class ASD diagnosis is developed by jointly using both views of the FC and PBFCT features. The experimental results using the ABIDE dataset verify the effectiveness of our method, which is capable of accurately classifying each subject into a respective ASD sub-category.

Multispectral pedestrian detection is an important task due to its critical role in a wide spectrum of applications. Basically, the complementary information from color and thermal images could provide a more accurate and reliable pedestrian detection result. However, multimodal data usually suffer from the issue of dynamic change or corruption for some modalities. At the same time, as a safety-critical task, how to produce a stable and reliable detection result is also a key challenge. To address these challenges, we propose a stable multispectral pedestrian detection (SMPD) algorithm, providing a new paradigm for multispectral detection by dynamically integrating different modalities at an evidence level. Specifically, we introduce the Dirichlet distribution to characterize the distribution of the class probabilities, parameterized with evidence from different modalities. Then, multi-branch fusion, based on Dempster-Shafer theory, can integrate these pieces of evidence to obtain the detection result. In addition, a Plug-and-Play module, termed modal enhancement module, is introduced to enhance cross-modality interaction. This is an end-to-end framework, which can induce accurate detection and uncertainty estimation, and then endows the model with both reliability and robustness against noise or corruption. Extensive experimental results demonstrate the efficiency of our algorithm compared with state-of-the-art methods.

For real-world applications, multilabel learning usually suffers from unsatisfactory training data. Typically, features may be corrupted or class labels may be noisy or both. Ignoring noise in the learning process tends to result in an unreasonable model and, thus, inaccurate prediction. Most existing methods only consider either feature noise or label noise in multilabel learning. In this paper, we propose a unified robust multilabel learning framework for data with hybrid noise, that is, both feature noise and label noise. The proposed method, hybrid noise-oriented multilabel learning (HNOML), is simple but rather robust for noisy data. HNOML simultaneously addresses feature and label noise by bi-sparsity regularization bridged with label enrichment. Specifically, the label enrichment matrix explores the underlying correlation among different classes which improves the noisy labeling. Bridged with the enriching label matrix, the structured sparsity is imposed to jointly handle the corrupted features and noisy labeling. We utilize the alternating direction method (ADM) to efficiently solve our problem. Experimental results on several benchmark datasets demonstrate the advantages of our method over the state-of-the-art ones.

Weiqi Wang, Tianqing Fang, Baixuan Xu, Chun Yi Louis Bo, Yangqiu Song, Lei Chen. Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2023.