Title | Representing the dynamics of high-dimensional data with non-redundant wavelets |
Authors | Jia, Shanshan Li, Xingyi Huang, Tiejun Liu, Jian K. Yu, Zhaofei |
Affiliation | Peking Univ, Inst Artificial Intelligence, Beijing 100871, Peoples R China Peking Univ, Dept Comp Sci & Technol, Beijing 100871, Peoples R China Chongqing Univ, Ctr Neurointelligence, Sch Med, Chongqing 400030, Peoples R China Univ Leeds, Sch Comp, Leeds LS2 9JT, W Yorkshire, England |
Keywords | EXTRACTING INFORMATION CORTEX ORGANIZATION PATTERNS SYNERGY SCENES CODES |
Issue Date | 11-Mar-2022 |
Publisher | PATTERNS |
Abstract | A crucial question in data science is to extract meaningful information embedded in high-dimensional data into a low-dimensional set of features that can represent the original data at different levels. Wavelet analysis is a pervasive method for decomposing time-series signals into a fewlevels with detailed temporal resolution. However, obtained wavelets are intertwined and over-represented across levels for each sample and across different samples within one population. Here, using neuroscience data of simulated spikes, experimental spikes, calcium imaging signals, and human electrocorticography signals, we leveraged conditional mutual information between wavelets for feature selection. The meaningfulness of selected features was verified to decode stimulus or condition with high accuracy yet using only a small set of features. These results provide a new way of wavelet analysis for extracting essential features of the dynamics of spatiotemporal neural data, which then enables to support novel model design of machine learning with representative features. |
URI | http://hdl.handle.net/20.500.11897/648657 |
ISSN | 2666-3899 |
DOI | 10.1016/j.patter.2021.100424 |
Indexed | EI ESCI |
Appears in Collections: | 信息科学技术学院 |