用随机傅里叶特征加速无监督时序分割，快278倍且精度不降
Scalable Unsupervised Segmentation via Random Fourier Feature-based Gaussian Process
- 用随机傅里叶特征近似高斯过程，避免大矩阵求逆
- 在39200帧数据上比传统方法快278倍，性能相当
- 适合处理大规模时序数据的快速无监督分割任务
本文提出RFF-GP-HSMM，一种基于随机傅里叶特征（RFF）的快速无监督时间序列分割方法，旨在解决高斯过程隐半马尔可夫模型（GP-HSMM）训练中因需对N×N核矩阵求逆而带来的高计算成本问题。该方法利用RFF将高斯过程近似为线性回归，保留强表达能力的同时消除了矩阵求逆需求。在卡内基梅隆大学（CMU）动作捕捉数据集上的实验表明，该方法在包含39,200帧的时间序列数据上实现的分割性能与传统方法相当，但速度提升约278倍。
原文摘要 · Abstract (English)
In this paper, we propose RFF-GP-HSMM, a fast unsupervised time-series segmentation method that incorporates random Fourier features (RFF) to address the high computational cost of the Gaussian process hidden semi-Markov model (GP-HSMM). GP-HSMM models time-series data using Gaussian processes, requiring inversion of an N times N kernel matrix during training, where N is the number of data points. As the scale of the data increases, matrix inversion incurs a significant computational cost. To address this, the proposed method approximates the Gaussian process with linear regression using RFF, preserving expressive power while eliminating the need for inversion of the kernel matrix. Experiments on the Carnegie Mellon University (CMU) motion-capture dataset demonstrate that the proposed method achieves segmentation performance comparable to that of conventional methods, with approximately 278 times faster segmentation on time-series data comprising 39,200 frames.
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