.png){kind=icon}
.png){kind=icon}
Return
Discriminative Dictionary Learning for Time Series Classification
.png){kind=icon}
OA
AI
DOI：10.1109/ACCESS.2020.3029140.png)
.png){kind=icon}
.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
Time series symbolization based on the Symbolic Fourier Approximation (SFA) and a sliding window mechanism can effectively improve classification performance. Hence, it has become a research hotspot of time series representation learning. However, there are still some obvious shortcomings. First, the lengths of the generated words are consistent for different sliding windows in the symbolization process, ignoring the difference of the discriminative information contained in diverse periods. Second, research on the relationship between words is limited to adjacent ones. Third, existing dictionary learning methods do not consider a filtering algorithm for discriminative word selection. To this end, a novel and fast variable-length word generation method, incorporating the skip-bigram model for the construction of symbiotic word pairs, is proposed for the first time in our work. Then, a discriminative word filter with a dynamic threshold is designed to build the discriminative word dictionary. Many controlled experiments first verified the effectiveness of each proposed method. Then, the performance improvement ability of the word dictionary is demonstrated by the comparative experiments with the representative classification models based on different theoretical foundations.
Keywords:
Time series analysis
Machine learning
Discrete Fourier transforms
Frequency-domain analysis
Dictionaries
Prototypes
Time series classification
symbolic Fourier approximation
skip-bigram
discriminative word filter
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.