.png){kind=icon}
.png){kind=icon}
Return
Robust Independent Component Analysis via Minimum γ-Divergence Estimation
.png){kind=icon}
PRE
AI
DOI：10.1109/JSTSP.2013.2247024.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
Independent component analysis (ICA) has been shown to be useful in many applications. However, most ICA methods are sensitive to data contamination. In this article we introduce a general minimum U-divergence framework for ICA, which covers some standard ICA methods as special cases. Within the U-family we further focus on the gamma-divergence due to its desirable property of super robustness for outliers, which gives the proposed method gamma-ICA. Statistical properties and technical conditions for recovery consistency of gamma-ICA are studied. In the limiting case, it improves the recovery condition of MLE-ICA known in the literature by giving necessary and sufficient condition. Since the parameter of interest in gamma-ICA is an orthogonal matrix, a geometrical algorithm based on gradient flows on special orthogonal group is introduced. Furthermore, a data-driven selection for the gamma value, which is critical to the achievement of gamma-ICA, is developed. The performance, especially the robustness, of gamma-ICA is demonstrated through experimental studies using simulated data and image data.
Keywords:
beta-divergence
gamma-divergence
geodesic
minimum divergence estimation
robust statistics
special orthogonal group
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
13.7
Papers:
1.9K
Citations:
1.1W
View more organizations