TY - GEN
T1 - Learning Scale and Shift-Invariant Dictionary for Sparse Representation
AU - Aritake, Toshimitsu
AU - Murata, Noboru
N1 - Funding Information:
Acknowledgement. This work was supported by JST CREST Grant Number JPMJCR1761 and JSPS KAKENHI Grant Numbers JP17H01793, JP18H03291.
Funding Information:
This work was supported by JST CREST Grant Number JPMJCR1761 and JSPS KAKENHI Grant Numbers JP17H01793, JP18H03291.
Publisher Copyright:
© Springer Nature Switzerland AG 2019.
PY - 2019
Y1 - 2019
N2 - 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.
AB - 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.
KW - Dictionary learning
KW - Scale-invariance
KW - Shift-invariance
KW - Sparse coding
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U2 - 10.1007/978-3-030-37599-7_39
DO - 10.1007/978-3-030-37599-7_39
M3 - Conference contribution
AN - SCOPUS:85078471158
SN - 9783030375980
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 472
EP - 483
BT - Machine Learning, Optimization, and Data Science - 5th International Conference, LOD 2019, Proceedings
A2 - Nicosia, Giuseppe
A2 - Pardalos, Panos
A2 - Umeton, Renato
A2 - Giuffrida, Giovanni
A2 - Sciacca, Vincenzo
PB - Springer
T2 - 5th International Conference on Machine Learning, Optimization, and Data Science, LOD 2019
Y2 - 10 September 2019 through 13 September 2019
ER -