TAILIEUCHUNG - Independent component analysis P17

Nonlinear ICA This chapter deals with independent component analysis (ICA) for nonlinear mixing models. A fundamental difficulty in the nonlinear ICA problem is that it is highly nonunique without some extra constraints, which are often realized by using a suitable regularization. We also address the nonlinear blind source separation (BSS) problem. Contrary to the linear case, we consider it different from the respective nonlinear ICA problem. After considering these matters, some methods introduced for solving the nonlinear ICA or BSS problems are discussed in more detail. Special emphasis is given to a Bayesian approach that applies ensemble learning to a flexible. | 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 17 Nonlinear ICA This chapter deals with independent component analysis ICA for nonlinear mixing models. A fundamental difficulty in the nonlinear ICA problem is that it is highly nonunique without some extra constraints which are often realized by using a suitable regularization. We also address the nonlinear blind source separation BSS problem. Contrary to the linear case we consider it different from the respective nonlinear ICA problem. After considering these matters some methods introduced for solving the nonlinear ICA or BSS problems are discussed in more detail. Special emphasis is given to a Bayesian approach that applies ensemble learning to a flexible multilayer perceptron model for finding the sources and nonlinear mixing mapping that have most probably given rise to the observed mixed data. The efficiency of this method is demonstrated using both artificial and real-world data. At the end of the chapter other techniques proposed for solving the nonlinear ICA and BSS problems are reviewed. NONLINEAR ICA AND BSS The nonlinear ICA and BSS problems In many situations the basic linear ICA or BSS model n x As j i is too simple for describing the observed data x adequately. Hence it is natural to consider extension of the linear model to nonlinear mixing models. For instantaneous 315 316 NONLINEAR ICA mixtures the nonlinear mixing model has the general form x f s where x is the observed m-dimensional data mixture vector f is an unknown realvalued m-component mixing function and s is an n-vector whose elements are the n unknown independent components. Assume now for simplicity that the number of independent components n equals the number of mixtures m. The general nonlinear ICA problem then consists of finding a mapping h R R that gives components y h x that are statistically

TÀI LIỆU LIÊN QUAN
31    426    56
TỪ KHÓA LIÊN QUAN
TAILIEUCHUNG - Chia sẻ tài liệu không giới hạn
Địa chỉ : 444 Hoang Hoa Tham, Hanoi, Viet Nam
Website : tailieuchung.com
Email : tailieuchung20@gmail.com
Tailieuchung.com là thư viện tài liệu trực tuyến, nơi chia sẽ trao đổi hàng triệu tài liệu như luận văn đồ án, sách, giáo trình, đề thi.
Chúng tôi không chịu trách nhiệm liên quan đến các vấn đề bản quyền nội dung tài liệu được thành viên tự nguyện đăng tải lên, nếu phát hiện thấy tài liệu xấu hoặc tài liệu có bản quyền xin hãy email cho chúng tôi.
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.