Independent component analysis P8

ICA by Maximization of Nongaussianity In this chapter, we introduce a simple and intuitive principle for estimating the model of independent component analysis (ICA). This is based on maximization of nongaussianity. Nongaussianity is actually of paramount importance in ICA estimation. Without nongaussianity the estimation is not possible at all, as shown in Section . Therefore, it is not surprising that nongaussianity could be used as a leading principle in ICA estimation. This is at the same time probably the main reason for the rather late resurgence of ICA research: In most of classic statistical theory, random variables are assumed to have. | Independent Component Analysis. Aapo Hyvarinen Juha Karhunen Erkki Oja Copyright 2001 John Wiley Sons Inc. ISBNs 0-471-40540-X Hardback 0-471-22131-7 Electronic 8 ICA by Maximization of Nongaussianity In this chapter we introduce a simple and intuitive principle for estimating the model of independent component analysis ICA . This is based on maximization of nongaussianity. Nongaussianity is actually of paramount importance in ICA estimation. Without nongaussianity the estimation is not possible at all as shown in Section . Therefore it is not surprising that nongaussianity could be used as a leading principle in ICA estimation. This is at the same time probably the main reason for the rather late resurgence of ICA research In most of classic statistical theory random variables are assumed to have gaussian distributions thus precluding methods related to ICA. A completely different approach may then be possible though using the time structure of the signals see Chapter 18. We start by intuitively motivating the maximization of nongaussianity by the central limit theorem. As a first practical measure of nongaussianity we introduce the fourth-order cumulant or kurtosis. Using kurtosis we derive practical algorithms by gradient and fixed-point methods. Next to solve some problems associated with kurtosis we introduce the information-theoretic quantity called negentropy as an alternative measure of nongaussianity and derive the corresponding algorithms for this measure. Finally we discuss the connection between these methods and the technique called projection pursuit. 165 166 ICA BYMAXIMIZATION OF NONGAUSSIANITY NONGAUSSIAN IS INDEPENDENT The central limit theorem is a classic result in probability theory that was presented in Section . It says that the distribution of a sum of independent random variables tends toward a gaussian distribution under certain conditions. Loosely speaking a sum of two independent random variables usually has a .

TÀI LIỆU LIÊN QUAN
9    196    0
31    942    42
1    833    74
89    230    12
80    304    25
51    240    8
95    377    38
1    378    17
78    197    15
91    156    6
TÀI LIỆU XEM NHIỀU
13    34775    1858
3    21970    227
25    20201    3869
20    17446    1494
16    17175    2656
14    15364    2660
1    13795    450
37    13723    2840
3    11949    226
23    11210    400
TỪ KHÓA LIÊN QUAN
TÀI LIỆU MỚI ĐĂNG
6    123    1    07-12-2022
61    13    1    07-12-2022
10    6    1    07-12-2022
103    2    1    07-12-2022
27    20    1    07-12-2022
10    1    1    07-12-2022
152    3    1    07-12-2022
7    36    1    07-12-2022
110    72    1    07-12-2022
10    11    1    07-12-2022
10    10    1    07-12-2022
101    17    1    07-12-2022
30    6    1    07-12-2022
105    10    1    07-12-2022
1    10    1    07-12-2022
6    10    1    07-12-2022
106    8    1    07-12-2022
14    10    1    07-12-2022
18    4    1    07-12-2022
14    20    1    07-12-2022
TÀI LIỆU HOT
3    21970    227
13    34775    1858
3    1807    76
580    3856    352
584    2132    88
62    4899    1
171    4286    642
2    2006    74
51    2803    158
53    3716    180
Đã phát hiện trình chặn quảng cáo AdBlock
Trang web này phụ thuộc vào doanh thu từ số lần hiển thị quảng cáo để tồn tại. Vui lòng tắt trình chặn quảng cáo của bạn hoặc tạm dừng tính năng chặn quảng cáo cho trang web này.