Distributionally Robust Semi-Supervised Learning for People-Centric Sensing
Semi-supervised learning is crucial for alleviating labelling burdens in people-centric sensing. However, human-generated data inherently suffer from distribution shift in semi-supervised learning due to the diverse biological conditions and behavior patterns of humans. To address this problem, we propose a generic distributionally robust model for semi-supervised learning on distributionally shifted data. Considering both the discrepancy and the consistency between the labeled data and the unlabeled data, we learn the latent features that reduce person-specific discrepancy and preserve task-specific consistency. We evaluate our model in a variety of people-centric recognition tasks on real-world datasets, including intention recognition, activity recognition, muscular movement recognition and gesture recognition. The experiment results demonstrate that the proposed model outperforms the state-of-the-art methods.
Code (0)
등록된 구현이 없습니다.
Tasks
Activity RecognitionGesture RecognitionIntent DetectionMuscular Movement RecognitionSimilar Papers 제목 키워드 기반
Distributionally Robust Inverse Reinforcement Learning for Identifying Multi-Agent Coordinated Sensing
We derive a minimax distributionally robust inverse reinforcement learning (IRL) algorithm to reconstruct the utility functions of a multi-agent sensing system. Specifically, we construct utility estimators which minimiz…
Semi-supervised Learning based on Distributionally Robust Optimization
We propose a novel method for semi-supervised learning (SSL) based on data-driven distributionally robust optimization (DRO) using optimal transport metrics. Our proposed method enhances generalization error by using the…
Dimensionality ReductionRotatable and Movable Antenna-Enabled Near-Field Integrated Sensing and Communication
The aim of this article is to investigate the performance of near-field integrated sensing and communication (ISAC) systems using rotatable movable antennas (RMAs). In the proposed RMA-enabled system, the positions and r…
Integrated sensing and communicationISACDistributionally Robust Learning
This monograph develops a comprehensive statistical learning framework that is robust to (distributional) perturbations in the data using Distributionally Robust Optimization (DRO) under the Wasserstein metric. Beginning…
Decision MakingregressionDeep Representation for Connected Health: Semi-supervised Learning for Analysing the Risk of Urinary Tract Infections in People with Dementia
Machine learning techniques combined with in-home monitoring technologies provide a unique opportunity to automate diagnosis and early detection of adverse health conditions in long-term conditions such as dementia. Howe…