Kernel Methods and the Maximum Mean Discrepancy for Seizure Detection

被引：0
|
作者：
Hamzi, Boumediene [1 ]
AlOtaiby, Turky N. [2 ]
AlShebeili, Saleh [3 ]
AlAnqary, Arwa [4 ]
机构：
[1] Alfaisal Univ, Dept Math, Riyadh, Saudi Arabia
[2] KACST, Riyadh, Saudi Arabia
[3] King Saud Univ, Dept Elect Engn, KACST TIC RF & Photon E Soc, Riyadh, Saudi Arabia
[4] KACST, CCES, Riyadh, Saudi Arabia
来源：
2018 1ST INTERNATIONAL CONFERENCE ON COMPUTER APPLICATIONS & INFORMATION SECURITY (ICCAIS' 2018) | 2018年
关键词：
METRIC-SPACES; PREDICTION; ILAE; LONG;
D O I：
暂无
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
We introduce a data-driven method for seizure detection drawing on recent progress in Machine Learning. The method is based on embedding probability measures in a high (or infinite) dimensional reproducing kernel Hilbert space (RKHS) where the Maximum Mean Discrepancy (MMD) is computed. The MMD is metric between probability measures that is computed as the difference between the means of probability measures after being embedded in an RKHS. Working in RKHS provides a convenient, general functional-analytical framework for theoretical understanding of data. We apply this approach to the problem of seizure detection.
引用
收藏
页数：6
相关论文
共 50 条
  • [1] Neural Tangent Kernel Maximum Mean Discrepancy
    Cheng, Xiuyuan
    Xie, Yao
    [J]. ADVANCES IN NEURAL INFORMATION PROCESSING SYSTEMS 34 (NEURIPS 2021), 2021, 34
  • [2] Integrating structured biological data by Kernel Maximum Mean Discrepancy
    Borgwardt, Karsten M.
    Gretton, Arthur
    Rasch, Malte J.
    Kriegel, Hans-Peter
    Schoelkopf, Bernhard
    Smola, Alex J.
    [J]. BIOINFORMATICS, 2006, 22 (14) : E49 - E57
  • [3] A coincidence detection perspective for the maximum mean discrepancy
    Montalvao, Jugurta
    Duarte, Dami
    Boccato, Levy
    [J]. PATTERN RECOGNITION LETTERS, 2024, 177 : 20 - 25
  • [4] Active label distribution learning via kernel maximum mean discrepancy
    Xinyue Dong
    Tingjin Luo
    Ruidong Fan
    Wenzhang Zhuge
    Chenping Hou
    [J]. Frontiers of Computer Science, 2023, 17
  • [5] Active label distribution learning via kernel maximum mean discrepancy
    Dong, Xinyue
    Luo, Tingjin
    Fan, Ruidong
    Zhuge, Wenzhang
    Hou, Chenping
    [J]. FRONTIERS OF COMPUTER SCIENCE, 2023, 17 (04)
  • [6] WLAN Indoor Intrusion Detection Approach Based on Multiple Kernel Maximum Mean Discrepancy Transfer Learning
    Zhou Mu
    Li Yaoping
    Xie Liangbo
    Pu Qiaolin
    Tian Zengshan
    [J]. JOURNAL OF ELECTRONICS & INFORMATION TECHNOLOGY, 2020, 42 (05) : 1149 - 1157
  • [7] Maximum Mean Discrepancy for Class Ratio Estimation: Convergence Bounds and Kernel Selection
    Iyer, Arun
    Nath, Saketh
    Sarawagi, Sunita
    [J]. INTERNATIONAL CONFERENCE ON MACHINE LEARNING, VOL 32 (CYCLE 1), 2014, 32
  • [8] Automatic Modulation Classification Based on CNN and Multiple Kernel Maximum Mean Discrepancy
    Wang, Na
    Liu, Yunxia
    Ma, Liang
    Yang, Yang
    Wang, Hongjun
    [J]. ELECTRONICS, 2023, 12 (01)
  • [9] Unsupervised Visual Changepoint Detection Using Maximum Mean Discrepancy
    Diu, Michael
    Gangeh, Mehrdad
    Kamel, Mohamed S.
    [J]. IMAGE ANALYSIS AND RECOGNITION, 2013, 7950 : 336 - 345
  • [10] Quantum maximum mean discrepancy GAN
    Huang, Yiming
    Lei, Hang
    Li, Xiaoyu
    Yang, Guowu
    [J]. NEUROCOMPUTING, 2021, 454 : 88 - 100
12345