|
|
|
题名
|
作者
|
年代
|
出处
|
被引量
|
| 1 | On the robustness of EEG tensor completion methods显示文摘During the acquisition of electroencephalographic(EEG) signals, data may be missing or corrupted by noise and artifacts. To reconstruct the incomplete data, EEG signals are firstly converted into a three-order tensor(multi-dimensional data) of shape time × channel × trial. Then, the missing data can be efficiently recovered by applying a tensor completion method(TCM).However, there is not a unique way to organize channels and trials in a tensor, and different numbers of channels are available depending on the EEG setting used, which may affect the quality of the tensor completion results. The main goal of this paper is to evaluate the robustness of EEG completion methods with several designed parameters such as the ordering of channels and trials, the number of channels, and the amount of missing data. In this work, the results of completing missing data by several TCMs were compared. To emulate different scenarios of missing data, three different patterns of missing data were designed.Firstly, the amount of missing data on completion effects was analyzed, including the time lengths of missing data and the number of channels or trials affected by missing data. Secondly, the numerical stability of the completion methods was analyzed by shuffling the indices along channels or trials in the EEG data tensor. Finally, the way that the number of electrodes of EEG tensors influences completion effects was assessed by changing the number of channels. Among all the applied TCMs, the simultaneous tensor decomposition and completion(STDC) method achieves the best performance in providing stable results when the amount of missing data or the electrode number of EEG tensors is changed. In other words, STDC proves to be an excellent choice of TCM, since permutations of trials or channels have almost no influence on the complete results. The STDC method can efficiently complete the missing EEG signals. The designed simulations can be regarded as a procedure to validate whether or not a completion method is useful enough to complete EEG signals. | DUAN Feng JIA Hao ZHANG ZhiWen FENG Fan TAN Ying DAI YangYang CICHOCKI Andrzej YANG ZhengLu CAIAFA Cesar F. SUN Zhe SOLE-CASALS Jordi | 2021 | Science China(Technological Sciences)2021，64，9： | 2 |
| 2 | Preface显示文摘Tensor decomposition and tensor networks(TNs)are factorizations of high order tensors into a network of low-order tensors,which have been studied in quantum physics,chemistry and applied mathematics.In recent years,TNs have been increasingly investigated and applied to machine learning and AI fields,due to its significant efficacy in modeling large-scale and high-order data,representing model parameters in deep neural networks,and accelerating computations for learning algorithms.In particular,TNs have been exploited to solve several challenging problems in data completion,model compression,multimodal fusion,multitask knowledge sharing and theoretical analysis of deep neural networks.More potential technologies using TNs are rapidly emerging and finding many interesting applications in machine learning,such as modeling probability functions,probabilistic graphical models and implementing efficient TN computations in GPU.However,the topic of TNs in machine learning is relatively young and many open problems are still not fully explored.This special topic aims to promote research and development related to innovative TNs technology from perspectives of fundamental theory and algorithms,novel approaches in machine learning and deep neural networks,and various applications in computer vision,biomedical image processing and many other related fields. | ZHAO QiBin ZHOU GuoXu ZHANG Yu CAIAFA Cesar F. CAO JianTing | 2021 | Science China(Technological Sciences)2021，64，9： | 0 |
| 3 | A hybrid method to select morphometric features using tensor completion and F-score rank for gifted children identification显示文摘Gifted children are able to learn in a more advanced way than others, probably due to neurophysiological differences in the communication efficiency in neural pathways. Topological features contribute to understanding the correlation between the brain structure and intelligence. Despite decades of neuroscience research using MRI, methods based on brain region connectivity patterns are limited by MRI artifacts, which therefore leads to revisiting MRI morphometric features, with the aim of using them to directly identify gifted children instead of using brain connectivity. However, the small, high-dimensional morphometric feature dataset with outliers makes the task of finding good classification models challenging. To this end, a hybrid method is proposed that combines tensor completion and feature selection methods to handle outliers and then select the discriminative features. The proposed method can achieve a classification accuracy of 93.1%, higher than other existing algorithms, which is thus suitable for the small MRI datasets with outliers in supervised classification scenarios. | ZHANG Jin FENG Fan HAN TianYi DUAN Feng SUN Zhe CAIAFA Cesar F. SOLÉ-CASALS Jordi | 2021 | Science China(Technological Sciences)2021，64，9： | 0 |
您所在的机构暂未开通此部分的功能，请联系管理员