# Scalable Unsupervised Segmentation via Random Fourier Feature-based Gaussian Process

arXiv: https://arxiv.org/abs/2507.10632 · HTML: https://mlsanity.com/paper/2507.10632/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 25 references that are in the map.
- First posted on arXiv 14 Jul 2025.

## Builds on (most cited first, 13 of 13)

- [Temporal Convolutional Networks for Action Segmentation and Detection](https://mlsanity.com/paper/1611.05267/) (2016)
- [Transformers in Time Series: A Survey](https://mlsanity.com/paper/2202.07125/) (2022)
- [Long-term Forecasting with TiDE: Time-series Dense Encoder](https://mlsanity.com/paper/2304.08424/) (2023)
- [End-to-end Learning of Action Detection from Frame Glimpses in Videos](https://mlsanity.com/paper/1511.06984/) (2015)
- [Connectionist Temporal Modeling for Weakly Supervised Action Labeling](https://mlsanity.com/paper/1607.08584/) (2016)
- [Weakly Supervised Action Learning with RNN based Fine-to-coarse Modeling](https://mlsanity.com/paper/1703.08132/) (2017)
- [Unsupervised Learning and Segmentation of Complex Activities from Video](https://mlsanity.com/paper/1803.09490/) (2018)
- [Weakly Supervised Action Labeling in Videos Under Ordering Constraints](https://mlsanity.com/paper/1407.1208/) (2014)
- [Spatio-Temporal Channel Correlation Networks for Action Classification](https://mlsanity.com/paper/1806.07754/) (2018)
- [Bayesian Non-linear Latent Variable Modeling via Random Fourier Features](https://mlsanity.com/paper/2306.08352/) (2023)
- [Preventing Model Collapse in Gaussian Process Latent Variable Models](https://mlsanity.com/paper/2404.01697/) (2024)
- [Temporal Segment Transformer for Action Segmentation](https://mlsanity.com/paper/2302.13074/) (2023)
- [Scalable Hybrid HMM with Gaussian Process Emission for Sequential Time-series Data Clustering](https://mlsanity.com/paper/2001.01917/) (2020)

## Built on by (most cited first, 1 of 1)

- [Random Fourier Features in Near-Linear Time](https://mlsanity.com/paper/2610.14751/) (2026)

## Abstract (arXiv)

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.