用t-SNE和形状片段学习提升触觉数据聚类精度
Making Sense of Touch: Unsupervised Shapelet Learning in Bag-of-words Sense
- 用t-SNE层将时序数据映射为形状片段归属概率，降维同时保留局部结构
- 在UCR数据集和电路元件操作任务中，聚类准确率优于现有先进方法
- 适合需要无监督触觉特征学习的机器人感知研究者
本文提出NN-STNE，一种利用t分布随机邻域嵌入（t-SNE）作为隐藏层的神经网络，通过将长时序数据映射为形状片段归属概率来降低输入维度。基于高斯核的均方误差损失函数保留了数据的局部结构，而K-means用于初始化形状片段候选，以应对非凸优化挑战。与现有方法不同，本方法使用t-SNE缓解低维空间中的拥挤问题，并采用L1正则化优化形状片段长度。在UCR数据集及电路元件操作任务（如开关操作）上的评估表明，其聚类准确率优于当前最优特征学习方法。
原文摘要 · Abstract (English)
This paper introduces NN-STNE, a neural network using t-distributed stochastic neighbor embedding (t-SNE) as a hidden layer to reduce input dimensions by mapping long time-series data into shapelet membership probabilities. A Gaussian kernel-based mean square error preserves local data structure, while K-means initializes shapelet candidates due to the non-convex optimization challenge. Unlike existing methods, our approach uses t-SNE to address crowding in low-dimensional space and applies L1-norm regularization to optimize shapelet length. Evaluations on the UCR dataset and an electrical component manipulation task, like switching on, demonstrate improved clustering accuracy over state-of-the-art feature-learning methods in robotics.
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