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Academy of Mathematics and Systems Science, CAS Colloquia & Seminars：How to capture tourists' search behavior in tourism forecasts？A two-stage feature selection approach
旅游预测 游客 搜索行为 两阶段 特征选择方法
2023/5/16
Search engine data have been widely used and shown to be useful in tourism demand forecasting. However, considering of the vast amounts of search keywords, how to better capture the tourists' attentio...
Greedy Feature Selection for Subspace Clustering
Subspace clustering unions of subspaces hybrid linear models sparse ap-proximation structured sparsity nearest neighbors low-rank approximation
2013/5/2
Unions of subspaces are powerful nonlinear signal models for collections of high-dimensional data. However, existing methods that exploit this structure require that the subspaces the signals of inter...
$l_{2,p}$ Matrix Norm and Its Application in Feature Selection
$l_{2,p}$ Matrix Norm Its Application Feature Selection
2013/5/2
Recently, $l_{2,1}$ matrix norm has been widely applied to many areas such as computer vision, pattern recognition, biological study and etc. As an extension of $l_1$ vector norm, the mixed $l_{2,1}$ ...
A Method for Avoiding Bias from Feature Selection with Application to Naive Bayes Classification Models
feature selection optimistic bias naive Bayes models gene expression data
2009/9/22
For many classication and regression problems, a large number of
features are available for possible use this is typical of DNA microarray data
on gene expression, for example. Often, for computatio...
Feature selection in omics prediction problems using cat scores and false non-discovery rate control
Feature selection omics prediction problems cat scores false non-discovery rate control
2010/3/18
We revisit the problem of feature selection in linear discriminant analysis (LDA),
i.e. when features are correlated. First, we introduce a pooled centroids formulation
of the multi-class LDA predic...
“Pre-conditioning” for feature selection and regression in high-dimensional problems
Pre-conditioning feature selection regression high-dimensional problems
2010/4/27
The primary method used for this initial regression is supervised principal components. Then we
apply a standard procedure such as forward stepwise selection or the
LASSO to the pre-conditioned resp...
A Method for Avoiding Bias from Feature Selection with Application to Naive Bayes Classification Models
Method Feature Selection Application Naive Bayes Classification Models
2010/4/26
For many classification and regression problems, a large number of features are available
for possible use — this is typical of DNA microarray data on gene expression, for example. Often,for computat...