کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
408390 | 679025 | 2007 | 11 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
Extending ICA for finding jointly dependent components from two related data sets
دانلود مقاله + سفارش ترجمه
دانلود مقاله ISI انگلیسی
رایگان برای ایرانیان
کلمات کلیدی
موضوعات مرتبط
مهندسی و علوم پایه
مهندسی کامپیوتر
هوش مصنوعی
پیش نمایش صفحه اول مقاله
![عکس صفحه اول مقاله: Extending ICA for finding jointly dependent components from two related data sets Extending ICA for finding jointly dependent components from two related data sets](/preview/png/408390.png)
چکیده انگلیسی
In this paper, we introduce some methods for finding mutually corresponding dependent components from two different but related data sets in an unsupervised (blind) manner. The basic idea is to generalize cross-correlation analysis by taking into account higher-order statistics. We propose independent component analysis (ICA) type extensions for the singular value decomposition of the cross-correlation matrix. They extend cross-correlation analysis in a similar manner as ICA extends standard principal component analysis for covariance matrices. We present experimental results demonstrating the usefulness of the proposed methods both for artificially generated data and for a cryptographic problem.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Neurocomputing - Volume 70, Issues 16–18, October 2007, Pages 2969–2979
Journal: Neurocomputing - Volume 70, Issues 16–18, October 2007, Pages 2969–2979
نویسندگان
Juha Karhunen, Tomas Ukkonen,