TY - JOUR
T1 - Gene expression data classification using exponential locality sensitive discriminant analysis
AU - Chunming Xu
JO - Journal of Information and Computing Science
VL - 3
SP - 210
EP - 215
PY - 2024
DA - 2024/01
SN - 12
DO - http://doi.org/
UR - https://global-sci.org/intro/article_detail/jics/22479.html
KW -  gene  expression  data  classification
KW -   dimensionality  reduction
KW -   locality  sensitive  discriminant 
analysis
KW -  exponential locality sensitive discriminant analysis 

AB -  Locality  sensitive  discriminant  analysis  is  a  typical  and  very  effective  graph-based 
dimensionality  reduction  method  which  has  been  successfully  applied  in  pattern  recognition  problems.  
LSDA aims to find a projection which maximizes the margin between data points from different classes at 
each local area. As a result, it can discover the local geometrical structure of the data samples. However, just 
as linear discriminant analysis, it has the small sample size (SSS) problem. To overcome this limitation, we 
propose  a  novel  exponential  locality  sensitive  discriminant  analysis  algorithm  in  this  paper.  The  proposed 
algorithm  can  make  nearby  objects  with  the  same  labels  in  the  input  space  also  nearby  in  the  new 
representation; while nearby objects with different labels in the input space should be far apart. In addition, it 
can also deal with the SSS problem.  The experiments on gene expression data sets verify the effectiveness of 
the proposed algorithm.