Measuring similarities between unlabeled time series trajectories is an important problem in many domains such as medicine, economics, and vision. It is often unclear what is the appropriate metric to use because of the complex nature of noise in the trajectories (e.g.

Measuring similarities between unlabeled time series trajectories is an important problem in many domains such as medicine, economics, and vision. It is often unclear what is the appropriate metric to use because of the complex nature of noise in the trajectories (e.g. Experts typically hand-craft or manually select a specific metric, such as Dynamic Time Warping (DTW), to apply on their data. In this paper, we propose an end-to-end framework, autowarp, that optimizes and learns a good metric given unlabeled trajectories. We define a flexible and differentiable family of warping metrics, which encompasses common metrics such as DTW, Edit Distance, Euclidean, etc. Autowarp then leverages the representation power of sequence autoencoders to optimize for a member of this warping family.

In this paper, authors proposed a metric called warping distance to measure the distance between raw sequence. BetaCV is optimized to learn the parameters in the metric and the robustness of this metric to initial guess of clustering is proven. Compared with using Euclidean distance between sequences' latent representation, the proposed method shows some potentials to get better clustering results. My main concerns include: 1. I think authors may underestimate the power of autoencoder.

Measuring similarities between unlabeled time series trajectories is an important problem in many domains such as medicine, economics, and vision. It is often unclear what is the appropriate metric to use because of the complex nature of noise in the trajectories (e.g. Experts typically hand-craft or manually select a specific metric, such as Dynamic Time Warping (DTW), to apply on their data. In this paper, we propose an end-to-end framework, autowarp, that optimizes and learns a good metric given unlabeled trajectories. We define a flexible and differentiable family of warping metrics, which encompasses common metrics such as DTW, Edit Distance, Euclidean, etc. Autowarp then leverages the representation power of sequence autoencoders to optimize for a member of this warping family.