Classification of Dangerous Situations for Small Sample Size Problem in Maintenance Decision Support Systems

2017 ◽  
pp. 338-345 ◽  
Author(s):  
Vladimir R. Milov ◽  
Andrey V. Savchenko
Keyword(s):  
Decision Support ◽  
Sample Size ◽  
Decision Support Systems ◽  
Support Systems ◽  
Small Sample Size ◽  
Small Sample ◽  
Small Sample Size Problem ◽  
Size Problem ◽  
Maintenance Decision

Weighted Piecewise LDA for Solving the Small Sample Size Problem in Face Verification

2007 ◽  
Vol 18 (2) ◽  
pp. 506-519 ◽  
Author(s):  
Marios Kyperountas ◽  
Anastasios Tefas ◽  
Ioannis Pitas
Keyword(s):  
Sample Size ◽  
Small Sample Size ◽  
Small Sample ◽  
Face Verification ◽  
Small Sample Size Problem ◽  
Size Problem

SUBSPACES VERSUS SUBMANIFOLDS: A COMPARATIVE STUDY IN SMALL SAMPLE SIZE PROBLEM

2009 ◽  
Vol 23 (03) ◽  
pp. 463-490 ◽  
Author(s):  
HONG HUANG ◽  
JIANWEI LI ◽  
HAILIANG FENG
Keyword(s):  
Face Recognition ◽  
Comparative Study ◽  
Sample Size ◽  
Small Sample Size ◽  
Small Sample ◽  
Locally Linear Embedding ◽  
Small Sample Size Problem ◽  
Size Problem ◽  
The Face ◽  
Locally Linear

Automatic face recognition is a challenging problem in the biometrics area, where the dimension of the sample space is typically larger than the number of samples in the training set and consequently the so-called small sample size problem exists. Recently, neuroscientists emphasized the manifold ways of perception, and showed the face images may reside on a nonlinear submanifold hidden in the image space. Many manifold learning methods, such as Isometric feature mapping, Locally Linear Embedding, and Locally Linear Coordination are proposed. These methods achieved the submanifold by collectively analyzing the overlapped local neighborhoods and all claimed to be superior to such subspace methods as Eigenfaces and Fisherfaces in terms of classification accuracy. However, in literature, no systematic comparative study for face recognition is performed among them. In this paper, we carry out a comparative study in face recognition among them, and the study considers theoretical aspects as well as simulations performed using CMU PIE and FERET face databases.

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