.png){kind=icon}
Return
A simple iterative algorithm for parsimonious binary kernel Fisher discrimination
.png){kind=icon}
PRE
AI
DOI：10.1007/s10044-009-0162-1.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
By applying recent results in optimisation theory variously known as optimisation transfer or majorise/minimise algorithms, an algorithm for binary, kernel, Fisher discriminant analysis is introduced that makes use of a non-smooth penalty on the coefficients to provide a parsimonious solution. The problem is converted into a smooth optimisation that can be solved iteratively with no greater overhead than iteratively re-weighted least-squares. The result is simple, easily programmed and is shown to perform, in terms of both accuracy and parsimony, as well as or better than a number of leading machine learning algorithms on two well-studied and substantial benchmarks.
Keywords:
Kernel machines
Fisher discriminant analysis
Majorise-minimise algorithms
Sparsity
Parsimony
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
2
Papers:
2.0K
Citations:
1.9K
Organization
Cited Papers
ShareSaveA fast kernel-based nonlinear discriminant analysis for multi-class problems.png)
PATTERN RECOGNITION
PATTERN RECOGNITIONIF7.6
PREAI
ShareSave
ShareSaveShareSaveShareSave
View more