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Article type: Research Article
Authors: Gao, Cuifanga; b; * | Li, Junjiea | Shen, Wanqianga; b | Yin, Pinga
Affiliations: [a] School of Science, Jiangnan University, Wuxi, Jiangsu, China | [b] Wuxi Engineering Research Center for Biocomputing, Wuxi, Jiangsu, China
Correspondence: [*] Corresponding author: Cuifang Gao, School of Science, Jiangnan University, 1800 Lihu Avenue, Wuxi, Jiangsu 21412, China. Tel.: +86 15961824126; E-mail: cuifang_gao@163.com.
Abstract: Dynamic Time Warping (DTW algorithm) provides an effective method to obtain the similarity between unequal-sized signals. However, it cannot directly deal with high-dimensional samples such as matrices. Expanding a matrix to one dimensional vector as the input data of DTW will decrease the measure accuracy because of the losing of position information in the matrix. Aiming at this problem, a two-dimensional dynamic time warping algorithm (2D-DTW) is proposed in this paper to directly measure the similarity between matrices. In 2D-DTW algorithm, a three dimensional distance-cuboid is constructed, and its mapped distance matrix is defined by cutting and compressing the distance-cuboid. By introducing the dynamic programming theory to search the shortest warping path in the mapped matrix, the corresponding shortest distance can be obtained as the expected similarity measure. The experimental results suggest that the performance of 2D-DTW distance is superior to the traditional Euclidean distance and can improve the similarity accuracy between matrices by introducing the warping alignment mechanisms. 2D-DTW algorithm extends the application ranges of traditional DTW and is especially suitable for high-dimensional data.
Keywords: Pattern recognition, similarity distance, dynamic programming, DTW
DOI: 10.3233/IDA-215908
Journal: Intelligent Data Analysis, vol. 26, no. 4, pp. 859-871, 2022
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