Symbolic Aggregate Features
Symbolic Aggregate Features is a dimensionality reduction technique in time series analysis that transforms continuous time series data into a discrete symbolic representation through piecewise aggregation and quantization, enabling efficient feature extraction and pattern discovery.
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1.3K
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96
Authors
46
Institutions
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2017–2026
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Aggregate features and ADABOOST for music classification
James Bergstra, Norman Casagrande, Dumitru Erhan et al. · Machine Learning · 2006 · 276 citations · Full text
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Chongqing Institute of Green and Intelligent Technology
Chongqing, China
1 h-index
1 publications
18 citations

Southeast University
Nanjing, China
1 h-index
4 publications
48 citations

Drexel University
Philadelphia, United States
1 h-index
1 publications
15 citations
Chengdu Institute of Biology
Chengdu, China
1 h-index
1 publications
18 citations

University of Hawaiʻi at Mānoa
Honolulu, United States
1 h-index
1 publications
230 citations

Universiti Sains Malaysia
George Town, Malaysia
1 h-index
6 publications
162 citations

Ochanomizu University
Tokyo, Japan
1 h-index
2 publications
28 citations
Nebraska Medical Center
Omaha, United States
1 h-index
1 publications
15 citations
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2 publications · 50 citations
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2 publications · 96 citations
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1 publications · 12 citations
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1 publications · 29 citations
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