# Personalized Convolutional Dictionary Learning of Physiological Time Series

arXiv: https://arxiv.org/abs/2503.07687 · HTML: https://mlsanity.com/paper/2503.07687/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 21 of its 71 references that are in the map.
- First posted on arXiv 10 Mar 2025.

## Builds on (most cited first, 21 of 21)

- [Communication-Efficient Learning of Deep Networks from Decentralized Data](https://mlsanity.com/paper/1602.05629/) (2016)
- [Neural Discrete Representation Learning](https://mlsanity.com/paper/1711.00937/) (2017)
- [Personalized Federated Learning with Moreau Envelopes](https://mlsanity.com/paper/2006.08848/) (2020)
- [Convex Optimization: Algorithms and Complexity](https://mlsanity.com/paper/1405.4980/) (2014)
- [Deep Learning for ECG Analysis: Benchmarks and Insights from PTB-XL](https://mlsanity.com/paper/2004.13701/) (2020)
- [Stochastic Polyak Step-size for SGD: An Adaptive Learning Rate for Fast Convergence](https://mlsanity.com/paper/2002.10542/) (2020)
- [Revisiting the Polyak step size](https://mlsanity.com/paper/1905.00313/) (2019)
- [Convolutional Dictionary Learning: A Comparative Review and New Algorithms](https://mlsanity.com/paper/1709.02893/) (2017)
- [New Algorithms for Learning Incoherent and Overcomplete Dictionaries](https://mlsanity.com/paper/1308.6273/) (2013)
- [Learning the Morphology of Brain Signals Using Alpha-Stable Convolutional Sparse Coding](https://mlsanity.com/paper/1705.08006/) (2017)
- [Personalized PCA: Decoupling Shared and Unique Features](https://mlsanity.com/paper/2207.08041/) (2022)
- [Motiflets -- Simple and Accurate Detection of Motifs in Time Series](https://mlsanity.com/paper/2206.03735/) (2022)
- [Multivariate Convolutional Sparse Coding for Electromagnetic Brain Signals](https://mlsanity.com/paper/1805.09654/) (2018)
- [Time Series Alignment with Global Invariances](https://mlsanity.com/paper/2002.03848/) (2020)
- [Analysis of Fast Structured Dictionary Learning](https://mlsanity.com/paper/1805.12529/) (2018)
- [Personalized Dictionary Learning for Heterogeneous Datasets](https://mlsanity.com/paper/2305.15311/) (2023)
- [Heterogeneous Matrix Factorization: When Features Differ by Datasets](https://mlsanity.com/paper/2305.17744/) (2023)
- [Group Invariant Dictionary Learning](https://mlsanity.com/paper/2007.07550/) (2020)
- [Deep neural networks on diffeomorphism groups for optimal shape reparameterization](https://mlsanity.com/paper/2207.11141/) (2022)
- [Spike Sorting by Convolutional Dictionary Learning](https://mlsanity.com/paper/1806.01979/) (2018)
- [Generalized Time Warping Invariant Dictionary Learning for Time Series Classification and Clustering](https://mlsanity.com/paper/2306.17690/) (2023)

## Built on by (most cited first, 2 of 2)

- [Self-Supervised Dynamical System Representations for Physiological Time-Series](https://mlsanity.com/paper/2512.00239/) (2025)
- [BioFormer: Rethinking Cross-Subject Generalization via Spectral Structural Alignment in Biomedical Time-Series](https://mlsanity.com/paper/2605.22468/) (2026)

## Abstract (arXiv)

Human physiological signals tend to exhibit both global and local structures: the former are shared across a population, while the latter reflect inter-individual variability. For instance, kinetic measurements of the gait cycle during locomotion present common characteristics, although idiosyncrasies may be observed due to biomechanical disposition or pathology. To better represent datasets with local-global structure, this work extends Convolutional Dictionary Learning (CDL), a popular method for learning interpretable representations, or dictionaries, of time-series data. In particular, we propose Personalized CDL (PerCDL), in which a local dictionary models local information as a personalized spatiotemporal transformation of a global dictionary. The transformation is learnable and can combine operations such as time warping and rotation. Formal computational and statistical guarantees for PerCDL are provided and its effectiveness on synthetic and real human locomotion data is demonstrated.