A Sphere-Description-Based Approach for Multiple-Instance Learning

Weakly Supervised AUC Optimization: A Unified Partial AUC Approach

Since acquiring perfect supervision is usually difficult, real-world machine learning tasks often confront inaccurate, incomplete, or inexact supervision, collectively referred to as weak supervision. In this work, we present WSAUC, a unified framework for weakly supervised AUC optimization problems, which covers noisy label learning, positive-unlabeled learning, multi-instance learning, and semi-supervised learning scenarios. Within the WSAUC framework, we first frame the AUC optimization problems in various weakly supervised scenarios as a common formulation of minimizing the AUC risk on contaminated sets, and demonstrate that the empirical risk minimization problems are consistent with the true AUC. Then, we introduce a new type of partial AUC, specifically, the reversed partial AUC (rpAUC), which serves as a robust training objective for AUC maximization in the presence of contaminated labels. WSAUC offers a universal solution for AUC optimization in various weakly supervised scenarios by maximizing the empirical rpAUC. Theoretical and experimental results under multiple settings support the effectiveness of WSAUC on a range of weakly supervised AUC optimization tasks.

Deep Learning in Diverse Intelligent Sensor Based Systems

Deep learning has become a predominant method for solving data analysis problems in virtually all fields of science and engineering. The increasing complexity and the large volume of data collected by diverse sensor systems have spurred the development of deep learning methods and have fundamentally transformed the way the data are acquired, processed, analyzed, and interpreted. With the rapid development of deep learning technology and its ever-increasing range of successful applications across diverse sensor systems, there is an urgent need to provide a comprehensive investigation of deep learning in this domain from a holistic view. This survey paper aims to contribute to this by systematically investigating deep learning models/methods and their applications across diverse sensor systems. It also provides a comprehensive summary of deep learning implementation tips and links to tutorials, open-source codes, and pretrained models, which can serve as an excellent self-contained reference for deep learning practitioners and those seeking to innovate deep learning in this space. In addition, this paper provides insights into research topics in diverse sensor systems where deep learning has not yet been well-developed, and highlights challenges and future opportunities. This survey serves as a catalyst to accelerate the application and transformation of deep learning in diverse sensor systems.

Instance elimination strategy for non-convex multiple-instance learning using sparse positive bags

In some multiple instance learning (MIL) applications, positive bags are sparse (i.e. containing only a small fraction of positive instances). To deal with the imbalanced data caused by these situations, we present a novel MIL method based on a small sphere and large margin approach (SSLM-MIL). Due to the introduction of a large margin, SSLM-MIL enforces the desired constraint that for all positive bags, there is at least one positive instance in each bag. Moreover, our framework is flexible to incorporate the non-convex optimization problem. Therefore, we can solve it using the concave-convex procedure (CCCP). Still, CCCP may be computationally inefficient for the number of external iterations. Inspired by the existing safe screening rules, which can effectively reduce computational time by discarding some inactive instances. In this paper, we propose a strategy to reduce the scale of the optimization problem. Specifically, we construct a screening rule in the inner solver and another rule for propagating screened instances between iterations of CCCP. To the best of our knowledge, this is the first attempt to introduce safe instance screening to a non-convex hypersphere support vector machine. Experiments on thirty-one benchmark datasets demonstrate the safety and effectiveness of our approach.