Learning Scale and Shift-Invariant Dictionary for Sparse Representation

Toshimitsu Aritake*, Noboru Murata

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contribution


Sparse representation is a signal model to represent signals with a linear combination of a small number of prototype signals called atoms, and a set of atoms is called a dictionary. The design of the dictionary is a fundamental problem for sparse representation. However, when there are scaled or translated features in the signals, unstructured dictionary models cannot extract such features. In this paper, we propose a structured dictionary model which is scale and shift-invariant to extract features which commonly appear in several scales and locations. To achieve both scale and shift invariance, we assume that atoms of a dictionary are generated from vectors called ancestral atoms by scaling and shift operations, and an algorithm to learn these ancestral atoms is proposed.

Original languageEnglish
Title of host publicationMachine Learning, Optimization, and Data Science - 5th International Conference, LOD 2019, Proceedings
EditorsGiuseppe Nicosia, Panos Pardalos, Renato Umeton, Giovanni Giuffrida, Vincenzo Sciacca
Number of pages12
ISBN (Print)9783030375980
Publication statusPublished - 2019
Event5th International Conference on Machine Learning, Optimization, and Data Science, LOD 2019 - Siena, Italy
Duration: 2019 Sept 102019 Sept 13

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume11943 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference5th International Conference on Machine Learning, Optimization, and Data Science, LOD 2019


  • Dictionary learning
  • Scale-invariance
  • Shift-invariance
  • Sparse coding

ASJC Scopus subject areas

  • Theoretical Computer Science
  • General Computer Science


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