Scheduled System Maintenance on May 29th, 2015:
IEEE Xplore will be upgraded between 11:00 AM and 10:00 PM EDT. During this time there may be intermittent impact on performance. We apologize for any inconvenience.
By Topic

Computational decomposition of molecular signatures based on blind source separation of non-negative dependent sources with NMF

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

3 Author(s)
Junying Zhang ; Electron. Eng. Res. Inst., Xidian Univ., Xi''an, China ; Le Wei ; Yue Wang

As a common feature in microarray profiling, gene expression profiles represent a composite of more than one distinct sources, which can severely decrease the sensitivity and specificity for the measurement of molecular signatures associated with different disease processes. Independent component analysis (ICA) is a widely applicable approach in blind source separation (BSS) but with limitations that the sources are independent, while a more common situation, which still happens in microarray profiles, is BSS where sources are not statistically independent. A novel idea of BSS is presented: it is a matrix factorization problem without enforcement of statistical characteristics on sources, while blind independent source separation is in fact matrix factorization, to factorize the observation matrix into a mixing matrix and a source matrix where the sources are independent. Since non-negative sources are meaningful in many applications including microarray profiling, we presented that blind non-negative source separation is essentially a matrix factorization, to factorize the observation matrix into a non-negative mixing matrix and a non-negative source matrix where the sources may be dependent. Non-negative matrix factorization (NMF) technique is applied to this non-negative source separation and is proven by a large number of computer simulations and by partial volume correction (PVC) experiments for real microarray data that it is effective when the sources are dependent with each other and/or are Gaussian distributed.

Published in:

Neural Networks for Signal Processing, 2003. NNSP'03. 2003 IEEE 13th Workshop on

Date of Conference:

17-19 Sept. 2003