mlsanityData to 7 Oct 2026

Personalized Dictionary Learning for Heterogeneous Datasets

arXiv 2305.15311 ↗First posted 24 May 2023cs.LG cs.CVcited by 5

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Abstract (arXiv)

We introduce a relevant yet challenging problem named Personalized Dictionary Learning (PerDL), where the goal is to learn sparse linear representations from heterogeneous datasets that share some commonality. In PerDL, we model each dataset's shared and unique features as global and local dictionaries. Challenges for PerDL not only are inherited from classical dictionary learning (DL), but also arise due to the unknown nature of the shared and unique features. In this paper, we rigorously formulate this problem and provide conditions under which the global and local dictionaries can be provably disentangled. Under these conditions, we provide a meta-algorithm called Personalized Matching and Averaging (PerMA) that can recover both global and local dictionaries from heterogeneous datasets. PerMA is highly efficient; it converges to the ground truth at a linear rate under suitable conditions. Moreover, it automatically borrows strength from strong learners to improve the prediction of weak learners. As a general framework for extracting global and local dictionaries, we show the application of PerDL in different learning tasks, such as training with imbalanced datasets and video surveillance.

Key facts. Cited by 5 papers in the map (data through 7 Oct 2026). Builds on 18 of its 42 references that are in the map. First posted on arXiv 24 May 2023.

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18 of 42 references.

References · 18

  1. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,592
  2. 2020Ditto: Fair and Robust Federated Learning Through Personalizationcited by 665
  3. 2020Personalized Federated Learning with Moreau Envelopescited by 612
  4. 2020Federated Learning with Matched Averagingcited by 564
  5. 2018Analyzing Federated Learning through an Adversarial Lenscited by 346
  6. 2020Think Locally, Act Globally: Federated Learning with Local and Global Representationscited by 322
  7. 2017No Spurious Local Minima in Nonconvex Low Rank Problems: A Unified Geometric Analysiscited by 127
  8. 2015Complete Dictionary Recovery over the Sphere I: Overview and the Geometric Picturecited by 49
  9. 2015Simple, Efficient, and Neural Algorithms for Sparse Codingcited by 37
  10. 2013Learning Sparsely Used Overcomplete Dictionaries via Alternating Minimizationcited by 24
  11. 2021The Internet of Federated Things (IoFT): A Vision for the Future and In-depth Survey of Data-driven Approaches for Federated Learningcited by 23
  12. 2013New Algorithms for Learning Incoherent and Overcomplete Dictionariescited by 22
6 more
  1. 2021GIFAIR-FL: A Framework for Group and Individual Fairness in Federated Learningcited by 21
  2. 2022Personalized PCA: Decoupling Shared and Unique Featurescited by 17
  3. 2018Exact Guarantees on the Absence of Spurious Local Minima for Non-negative Rank-1 Robust Principal Component Analysiscited by 13
  4. 2022Simple Alternating Minimization Provably Solves Complete Dictionary Learningcited by 6
  5. 2018Analysis of Fast Structured Dictionary Learningcited by 5
  6. 2017Alternating minimization for dictionary learning: Local Convergence Guaranteescited by 4

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Cited by year

Citing papers per year: 2024: 4; 2025: 1.

5 papers.

  1. 2024Collaborative and Federated Black-box Optimization: A Bayesian Optimization Perspectivecited by 5
  2. 2024Personalized Coupled Tensor Decomposition for Multimodal Data Fusion: Uniqueness and Algorithmscited by 2
  3. 2025Personalized Convolutional Dictionary Learning of Physiological Time Seriescited by 2
  4. 2024Dataset Dictionary Learning in a Wasserstein Space for Federated Domain Adaptationcited by 1
  5. 2024Atom dimension adaptation for infinite set dictionary learningcited by 0