Principal component analysis
Principal component analysis is a versatile statistical method for reducing a cases-by-
variables data table to its essential features, called principal components. Principal …
variables data table to its essential features, called principal components. Principal …
Correlation and association analyses in microbiome study integrating multiomics in health and disease
Y ** branch of nonlinear optimization. Its focus
is on problems where the smooth geometry of the search space can be leveraged to design …
is on problems where the smooth geometry of the search space can be leveraged to design …
[PDF][PDF] Linear dimensionality reduction: Survey, insights, and generalizations
Linear dimensionality reduction methods are a cornerstone of analyzing high dimensional
data, due to their simple geometric interpretations and typically attractive computational …
data, due to their simple geometric interpretations and typically attractive computational …
[BOOK][B] MM optimization algorithms
K Lange - 2016 - SIAM
Algorithms have never been more important. As the recipes of computer programs,
algorithms rule our lives. Although they can be forces for both good and evil, this is not a …
algorithms rule our lives. Although they can be forces for both good and evil, this is not a …
[HTML][HTML] Improving malicious URLs detection via feature engineering: Linear and nonlinear space transformation methods
In malicious URLs detection, traditional classifiers are challenged because the data volume
is huge, patterns are changing over time, and the correlations among features are …
is huge, patterns are changing over time, and the correlations among features are …
[BOOK][B] Statistical foundations of data science
Statistical Foundations of Data Science gives a thorough introduction to commonly used
statistical models, contemporary statistical machine learning techniques and algorithms …
statistical models, contemporary statistical machine learning techniques and algorithms …