Change-in-velocity detection for multidimensional data

2025-10-30

AI Summary

This paper addresses the detection of velocity change-points in multidimensional single-molecule trajectories. We propose CPLASS, a segmented linear modeling framework that integrates a velocity penalty mechanism and a cumulative velocity allocation statistic to ensure both biophysical interpretability and large-sample consistency. To navigate the high-dimensional parameter space, CPLASS employs a Markov chain Monte Carlo search strategy augmented with a customized proposal distribution and a problem-specific penalty function, enabling efficient optimization and adaptive model selection. Experiments demonstrate that CPLASS robustly identifies biologically meaningful velocity transitions in subcellular transport, accurately discriminates between distinct molecular motor–driven transport regimes, effectively suppresses noise-induced artifacts, and significantly improves change-point localization accuracy and biological interpretability—making it particularly suitable for dissecting dynamic processes in complex biophysical environments.

Technology Category

• Search and Optimization: Sampling/Simulation-based Search
• Machine Learning: Learning with Manifolds
• Computer Vision: Motion & Tracking

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• User Modeling, Personalization and Recommendation: ML for personalized search and recommendations
• Security and Privacy: Large-scale security measurements

Abstract

In this work, we introduce CPLASS (Continuous Piecewise-Linear Approximation via Stochastic Search), an algorithm for detecting changes in velocity within multidimensional data. The one-dimensional version of this problem is known as the change-in-slope problem (see Fearnhead & Grose (2022), Baranowski et al. (2019)). Unlike traditional changepoint detection methods that focus on changes in mean, detecting changes in velocity requires a specialized approach due to continuity constraints and parameter dependencies, which frustrate popular algorithms like binary segmentation and dynamic programming. To overcome these difficulties, we introduce a specialized penalty function to balance improvements in likelihood due to model complexity, and a Markov Chain Monte Carlo (MCMC)-based approach with tailored proposal mechanisms for efficient parameter exploration. Our method is particularly suited for analyzing intracellular transport data, where the multidimensional trajectories of microscale cargo are driven by teams of molecular motors that undergo complex biophysical transitions. To ensure biophysical realism in the results, we introduce a speed penalty that discourages overfitted of short noisy segments while maintaining consistency in the large-sample limit. Additionally, we introduce a summary statistic called the Cumulative Speed Allocation, which is robust with respect to idiosyncracies of changepoint detection while maintaining the ability to discriminate between biophysically distinct populations.

Problem

• Detects velocity changes in multidimensional data trajectories
• Overcomes limitations of traditional changepoint detection methods
• Specifically designed for analyzing intracellular transport data

Innovation

• MCMC-based approach with tailored proposal mechanisms
• Specialized penalty function balancing likelihood and complexity
• Speed penalty ensuring biophysical realism in results