Making Sense of Touch: Unsupervised Shapelet Learning in Bag-of-words Sense

Zhicong Xian; Tabish Chaudhary; Jürgen Bock

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arXiv · 2502.04167

Making Sense of Touch: Unsupervised Shapelet Learning in Bag-of-words Sense

Zhicong Xian·Tabish Chaudhary·Jürgen Bock

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Abstract

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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BibTeX RIS

Zhicong Xian, Tabish Chaudhary, Jürgen Bock. 2025-02-06. Making Sense of Touch: Unsupervised Shapelet Learning in Bag-of-words Sense. https://arxiv.org/abs/2502.04167

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