用低秩张量核机实现高效癫痫检测迁移学习，参数少100倍。
Adapting Tensor Kernel Machines to Enable Efficient Transfer Learning for Seizure Detection
- 基于张量核机设计自适应迁移学习框架，通过正则化迁移源知识
- 仅需少量患者数据即可达到比通用与全定制模型更优的检测效果
- 参数量仅为SVM的1/100，适合可穿戴设备实时应用
迁移学习旨在通过相关源任务的学习来优化目标任务性能。本文提出一种基于张量核机的高效迁移学习方法，受自适应SVM启发，通过正则化将源域‘知识’迁移到适配模型中。张量核机的核心优势在于利用低秩张量网络在原始空间学习紧凑的非线性模型，实现高效适配且不增加模型参数。为验证方法有效性，我们将自适应张量核机（Adapt-TKM）应用于耳后脑电图（EEG）的癫痫检测。通过少量患者特异性数据个性化通用模型后，患者适配模型在性能上优于通用模型和完全患者特异模型。尤为关键的是，该方法仅需约自适应SVM模型1/100的参数量，推理速度显著提升，适用于资源受限的可穿戴设备。
原文摘要 · Abstract (English)
Transfer learning aims to optimize performance in a target task by learning from a related source problem. In this work, we propose an efficient transfer learning method using a tensor kernel machine. Our method takes inspiration from the adaptive SVM and hence transfers 'knowledge' from the source to the 'adapted' model via regularization. The main advantage of using tensor kernel machines is that they leverage low-rank tensor networks to learn a compact non-linear model in the primal domain. This allows for a more efficient adaptation without adding more parameters to the model. To demonstrate the effectiveness of our approach, we apply the adaptive tensor kernel machine (Adapt-TKM) to seizure detection on behind-the-ear EEG. By personalizing patient-independent models with a small amount of patient-specific data, the patient-adapted model (which utilizes the Adapt-TKM), achieves better performance compared to the patient-independent and fully patient-specific models. Notably, it is able to do so while requiring around 100 times fewer parameters than the adaptive SVM model, leading to a correspondingly faster inference speed. This makes the Adapt-TKM especially useful for resource-constrained wearable devices.
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