Scalable Unsupervised Segmentation via Random Fourier Feature-based Gaussian Process
This addresses a scalability problem for researchers and practitioners in time-series analysis, but it is incremental as it builds on existing GP-HSMM methods.
The paper tackled the high computational cost of Gaussian process hidden semi-Markov models for time-series segmentation by proposing RFF-GP-HSMM, which uses random Fourier features to approximate the Gaussian process, achieving comparable segmentation performance with about 278 times faster processing on a dataset with 39,200 frames.
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.