Sciweavers

Share
100 search results - page 1 / 20
» Regularized discriminant analysis for high dimensional, low ...
Sort
View
KDD
2006
ACM
149views Data Mining» more  KDD 2006»
10 years 11 months ago
Regularized discriminant analysis for high dimensional, low sample size data
Linear and Quadratic Discriminant Analysis have been used widely in many areas of data mining, machine learning, and bioinformatics. Friedman proposed a compromise between Linear ...
Jieping Ye, Tie Wang
ML
2015
ACM
4 years 6 months ago
Random projections as regularizers: learning a linear discriminant from fewer observations than dimensions
We prove theoretical guarantees for an averaging-ensemble of randomly projected Fisher Linear Discriminant classi´Čüers, focusing on the case when there are fewer training observat...
Robert J. Durrant, Ata Kabán
JMLR
2006
148views more  JMLR 2006»
9 years 10 months ago
Computational and Theoretical Analysis of Null Space and Orthogonal Linear Discriminant Analysis
Dimensionality reduction is an important pre-processing step in many applications. Linear discriminant analysis (LDA) is a classical statistical approach for supervised dimensiona...
Jieping Ye, Tao Xiong
PR
2008
144views more  PR 2008»
9 years 10 months ago
Kernel quadratic discriminant analysis for small sample size problem
It is generally believed that quadratic discriminant analysis (QDA) can better fit the data in practical pattern recognition applications compared to linear discriminant analysis ...
Jie Wang, Konstantinos N. Plataniotis, Juwei Lu, A...
SIAMMAX
2010
189views more  SIAMMAX 2010»
9 years 5 months ago
Fast Algorithms for the Generalized Foley-Sammon Discriminant Analysis
Linear Discriminant Analysis (LDA) is one of the most popular approaches for feature extraction and dimension reduction to overcome the curse of the dimensionality of the high-dime...
Lei-Hong Zhang, Li-Zhi Liao, Michael K. Ng
books