2010 Fiscal Year Final Research Report
Classification, Similarity Search, and Labeling of Time Series Data
| Project/Area Number |
20500139
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| Research Category |
Grant-in-Aid for Scientific Research (C)
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| Allocation Type | Single-year Grants |
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| Section | 一般 |
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| Research Field |
Intelligent informatics
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| Research Institution | Hiroshima City University |
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Principal Investigator |
HAYASHI Akira Hiroshima City University, 情報科学研究科, 教授 (60240909)
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| Project Period (FY) |
2008 – 2010
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| Keywords | 時系列データ / カーネル法 / DTW / 正定値計画法 / 隠れマルコフモデル / CRF / ラベルづけ |
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| Research Abstract |
1. We have developed kernels for time series data using dynamic time warping (DTW) distances. We use semidefinite programming to guarantee the positive definiteness of a kernel matrix. We use two applications, time series classification and time series embedding for similarity search to validate our approach.
2. We have proposed HHCRFs (hierarchical hidden CRFs). In the experiment, we show that HHCRFs perform better than HHMMs (hierarchical hidden Markov models) in state sequence estimation, as the training set size becomes larger and the data source becomes non-Markovian.
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