Heuristic search algorithm for dimensionality reduction optimally combining feature selection and feature extraction
The following are two classical approaches to dimensionality reduction: 1. Approximating
the data with a small number of features that exist in the data (feature selection). 2 …
the data with a small number of features that exist in the data (feature selection). 2 …
The art of centering without centering for robust principal component analysis
G Wan, B He, H Schweitzer - Data Mining and Knowledge Discovery, 2024 - Springer
Many robust variants of Principal Component Analysis remove outliers from the data and
compute the principal components of the remaining data. The robust centered variant …
compute the principal components of the remaining data. The robust centered variant …
A lookahead algorithm for robust subspace recovery
G Wan, H Schweitzer - 2021 IEEE international conference on …, 2021 - ieeexplore.ieee.org
A common task in the analysis of data is to compute an approximate embedding of the data
in a low-dimensional subspace. The standard algorithm for computing this subspace is the …
in a low-dimensional subspace. The standard algorithm for computing this subspace is the …
Accelerated combinatorial search for outlier detection with provable bound on sub-optimality
G Wan, H Schweitzer - Proceedings of the AAAI Conference on Artificial …, 2021 - ojs.aaai.org
Outliers negatively affect the accuracy of data analysis. In this paper we are concerned with
their influence on the accuracy of Principal Component Analysis (PCA). Algorithms that …
their influence on the accuracy of Principal Component Analysis (PCA). Algorithms that …
[PDF][PDF] AI Inspired Algorithms for Several Combinatorial Optimization Problems in Data Science
G Wan - 2021 - utd-ir.tdl.org
Combinatorial optimization is a class of problems that consists of finding an optimal solution
from a finite set of feasible solutions. Many important problems in Data Science can be …
from a finite set of feasible solutions. Many important problems in Data Science can be …
A bias trick for centered robust principal component analysis (student abstract)
A Bias Trick for Centered Robust Principal Component Analysis (Student Abstract) Page 1 The
Thirty-Fourth AAAI Conference on Artificial Intelligence (AAAI-20) A Bias Trick for Centered …
Thirty-Fourth AAAI Conference on Artificial Intelligence (AAAI-20) A Bias Trick for Centered …
Improving the accuracy of principal component analysis by the maximum entropy method
Classical Principal Component Analysis (PCA) approximates data in terms of projections on
a small number of orthogonal vectors. There are simple procedures to efficiently compute …
a small number of orthogonal vectors. There are simple procedures to efficiently compute …
[图书][B] Algorithms for Robust Data Analysis
B He - 2020 - search.proquest.com
Data analysis plays an important role in making decisions, making predictions, and helping
business operate. Unfortunately, in many situations the data is not reliable and robust …
business operate. Unfortunately, in many situations the data is not reliable and robust …
[图书][B] Feature Selection and Extraction-Algorithms and Applications
SR Shah - 2019 - search.proquest.com
Feature selection is a very important process in statistics and machine learning. It removes
redundant and irrelevant features and selects the most useful set of features from a given …
redundant and irrelevant features and selects the most useful set of features from a given …
A Bias Trick for Centered Robust Principal Component Analysis
arXiv:1911.08024v1 [cs.LG] 19 Nov 2019 Page 1 arXiv:1911.08024v1 [cs.LG] 19 Nov 2019 A
Bias Trick for Centered Robust Principal Component Analysis Baokun He, Guihong Wan, and …
Bias Trick for Centered Robust Principal Component Analysis Baokun He, Guihong Wan, and …