This work presents the design, implementation and validation of learning
techniques based on the kNN scheme for gesture detection in prosthetic control.
To cope with high computational demands in instance-based prediction, methods
of dataset reduction are evaluated considering real-time determinism to allow
for the reliable integration into battery-powered portable devices. The
influence of parameterization and varying proportionality schemes is analyzed,
utilizing an eight-channel-sEMG armband. Besides offline cross-validation
accuracy, success rates in real-time pilot experiments (online target
achievement tests) are determined. Based on the assessment of specific dataset
reduction techniques' adequacy for embedded control applications regarding
accuracy and timing behaviour, Decision Surface Mapping (DSM) proves itself
promising when applying kNN on the reduced set. A randomized, double-blind user
study was conducted to evaluate the respective methods (kNN and kNN with
DSM-reduction) against Ridge Regression (RR) and RR with Random Fourier
Features (RR-RFF). The kNN-based methods performed significantly better
(p<0.0005) than the regression techniques. Between DSM-kNN and kNN, there was
no statistically significant difference (significance level 0.05). This is
remarkable in consideration of only one sample per class in the reduced set,
thus yielding a reduction rate of over 99% while preserving success rate. The
same behaviour could be confirmed in an extended user study. With k=1, which
turned out to be an excellent choice, the runtime complexity of both kNN (in
every prediction step) as well as DSM-kNN (in the training phase) becomes
linear concerning the number of original samples, favouring dependable wearable
prosthesis applications.

基于 kNN 方案的学习技术在假肢控制中实现了姿势检测，通过评估数据集减小的方法，确保了高效的实时决策，从而使其能够可靠地集成到电池供电的便携设备中。

实例化学习与原型缩减在实时比例肌电控制中的应用：一个随机用户研究展示了为假肢嵌入式系统实现精确性保持的数据缩减

Instance-based Learning with Prototype Reduction for Real-Time  Proportional Myocontrol: A Randomized User Study Demonstrating  Accuracy-preserving Data Reduction for Prosthetic Embedded Systems

Training neural networks on a large dataset requires substantial
computational costs. Dataset reduction selects or synthesizes data instances
based on the large dataset, while minimizing the degradation in generalization
performance from the full dataset. Existing methods utilize the neural network
during the dataset reduction procedure, so the model parameter becomes
important factor in preserving the performance after reduction. By depending
upon the importance of parameters, this paper introduces a new reduction
objective, coined LCMat, which Matches the Loss Curvatures of the original
dataset and reduced dataset over the model parameter space, more than the
parameter point. This new objective induces a better adaptation of the reduced
dataset on the perturbed parameter region than the exact point matching.
Particularly, we identify the worst case of the loss curvature gap from the
local parameter region, and we derive the implementable upper bound of such
worst-case with theoretical analyses. Our experiments on both coreset selection
and condensation benchmarks illustrate that LCMat shows better generalization
performances than existing baselines.

使用新的 LCMat 目标函数，匹配神经网络模型参数空间中原始数据集和减小后的数据集的损失曲率，以提高数据集减小的泛化性能。实验结果表明，LCMat 相较于现有基线算法具有更好的性能。

数据集选择和压缩的损失曲率匹配

Loss-Curvature Matching for Dataset Selection and Condensation

In this paper, we propose a new measure to gauge the complexity of image
classification problems. Given an annotated image dataset, our method computes
a complexity measure called the cumulative spectral gradient (CSG) which
strongly correlates with the test accuracy of convolutional neural networks
(CNN). The CSG measure is derived from the probabilistic divergence between
classes in a spectral clustering framework. We show that this metric correlates
with the overall separability of the dataset and thus its inherent complexity.
As will be shown, our metric can be used for dataset reduction, to assess which
classes are more difficult to disentangle, and approximate the accuracy one
could expect to get with a CNN. Results obtained on 11 datasets and three CNN
models reveal that our method is more accurate and faster than previous
complexity measures.

本研究提出了一种测量图像分类问题复杂性的新方法，即累积谱梯度（CSG）度量，通过概率分布与谱聚类框架计算自然地反映数据集整体可分离性的度量，并通过 11 个数据集与三个 CNN 模型展示了其精度和快速性。