Feature Selection and Feature Extraction in Pattern Analysis: A Literature Review
Not on the map. Why?
Abstract (arXiv)
Pattern analysis often requires a pre-processing stage for extracting or selecting features in order to help the classification, prediction, or clustering stage discriminate or represent the data in a better way. The reason for this requirement is that the raw data are complex and difficult to process without extracting or selecting appropriate features beforehand. This paper reviews theory and motivation of different common methods of feature selection and extraction and introduces some of their applications. Some numerical implementations are also shown for these methods. Finally, the methods in feature selection and extraction are compared.
Key facts. Cited by 15 papers in the map (data through 7 Oct 2026). First posted on arXiv 7 May 2019.
Builds on
No reference list yet.
Built on by
Citing papers per year: 2021: 5; 2022: 2; 2023: 4; 2024: 2; 2025: 2.
15 papers.
Sort:
By area:
- 2021Reproducing Kernel Hilbert Space, Mercer's Theorem, Eigenfunctions, Nystr"om Method, and Use of Kernels in Machine Learning: Tutorial and Surveycited by 36
- 2023A Review of the Trends and Challenges in Adopting Natural Language Processing Methods for Education Feedback Analysiscited by 12
- 2021Laplacian-Based Dimensionality Reduction Including Spectral Clustering, Laplacian Eigenmap, Locality Preserving Projection, Graph Embedding, and Diffusion Map: Tutorial and Surveycited by 8
- 2023Transformer Multivariate Forecasting: Less is More?cited by 5
- 2025Machine Learning Methods for Small Data and Upstream Bioprocessing Applications: A Comprehensive Reviewcited by 4
- 2023Model-based feature selection for neural networks: A mixed-integer programming approachcited by 2
- 2024Semantic-Preserving Feature Partitioning for Multi-View Ensemble Learningcited by 2
- 2024Enhancing Classification Performance via Reinforcement Learning for Feature Selectioncited by 2
- 2025Sample-Efficient Behavior Cloning Using General Domain Knowledgecited by 2
- 2021Generative Locally Linear Embeddingcited by 1
- 2021Unified Framework for Spectral Dimensionality Reduction, Maximum Variance Unfolding, and Kernel Learning By Semidefinite Programming: Tutorial and Surveycited by 1
- 2022DPDR: A novel machine learning method for the Decision Process for Dimensionality Reductioncited by 1
3 more
- 2021Hierarchical Subspace Learning for Dimensionality Reduction to Improve Classification Accuracy in Large Data Setscited by 0
- 2022Deep-MDS Framework for Recovering the 3D Shape of 2D Landmarks from a Single Imagecited by 0
- 2023A Principled Hierarchical Deep Learning Approach to Joint Image Compression and Classificationcited by 0
- 2025Machine Learning Methods for Small Data and Upstream Bioprocessing Applications: A Comprehensive Review14 Jun 2025
- 2025Sample-Efficient Behavior Cloning Using General Domain Knowledge27 Jan 2025
- 2024Enhancing Classification Performance via Reinforcement Learning for Feature Selection9 Mar 2024
- 2024Semantic-Preserving Feature Partitioning for Multi-View Ensemble Learning11 Jan 2024
- 2023Transformer Multivariate Forecasting: Less is More?30 Dec 2023
- 2023A Principled Hierarchical Deep Learning Approach to Joint Image Compression and Classification30 Oct 2023
- 2023Model-based feature selection for neural networks: A mixed-integer programming approach20 Feb 2023
- 2023A Review of the Trends and Challenges in Adopting Natural Language Processing Methods for Education Feedback Analysis20 Jan 2023
- 2022Deep-MDS Framework for Recovering the 3D Shape of 2D Landmarks from a Single Image27 Oct 2022
- 2022DPDR: A novel machine learning method for the Decision Process for Dimensionality Reduction17 Jun 2022
- 2021Unified Framework for Spectral Dimensionality Reduction, Maximum Variance Unfolding, and Kernel Learning By Semidefinite Programming: Tutorial and Survey29 Jun 2021
- 2021Reproducing Kernel Hilbert Space, Mercer's Theorem, Eigenfunctions, Nystr"om Method, and Use of Kernels in Machine Learning: Tutorial and Survey15 Jun 2021
3 more
- 2021Laplacian-Based Dimensionality Reduction Including Spectral Clustering, Laplacian Eigenmap, Locality Preserving Projection, Graph Embedding, and Diffusion Map: Tutorial and Survey3 Jun 2021
- 2021Hierarchical Subspace Learning for Dimensionality Reduction to Improve Classification Accuracy in Large Data Sets25 May 2021
- 2021Generative Locally Linear Embedding4 Apr 2021
- 2021Reproducing Kernel Hilbert Space, Mercer's Theorem, Eigenfunctions, Nystr"om Method, and Use of Kernels in Machine Learning: Tutorial and Surveycited by 36
- 2023A Review of the Trends and Challenges in Adopting Natural Language Processing Methods for Education Feedback Analysiscited by 12
- 2021Laplacian-Based Dimensionality Reduction Including Spectral Clustering, Laplacian Eigenmap, Locality Preserving Projection, Graph Embedding, and Diffusion Map: Tutorial and Surveycited by 8
- 2023Transformer Multivariate Forecasting: Less is More?cited by 5
- 2025Machine Learning Methods for Small Data and Upstream Bioprocessing Applications: A Comprehensive Reviewcited by 4
- 2023Model-based feature selection for neural networks: A mixed-integer programming approachcited by 2
- 2024Semantic-Preserving Feature Partitioning for Multi-View Ensemble Learningcited by 2
- 2024Enhancing Classification Performance via Reinforcement Learning for Feature Selectioncited by 2
- 2021Generative Locally Linear Embeddingcited by 1
- 2021Unified Framework for Spectral Dimensionality Reduction, Maximum Variance Unfolding, and Kernel Learning By Semidefinite Programming: Tutorial and Surveycited by 1
- 2022DPDR: A novel machine learning method for the Decision Process for Dimensionality Reductioncited by 1
- 2021Hierarchical Subspace Learning for Dimensionality Reduction to Improve Classification Accuracy in Large Data Setscited by 0
12 most cited of 14 All 14 (JSON)
- 2025Machine Learning Methods for Small Data and Upstream Bioprocessing Applications: A Comprehensive Review14 Jun 2025
- 2024Enhancing Classification Performance via Reinforcement Learning for Feature Selection9 Mar 2024
- 2024Semantic-Preserving Feature Partitioning for Multi-View Ensemble Learning11 Jan 2024
- 2023Transformer Multivariate Forecasting: Less is More?30 Dec 2023
- 2023A Principled Hierarchical Deep Learning Approach to Joint Image Compression and Classification30 Oct 2023
- 2023Model-based feature selection for neural networks: A mixed-integer programming approach20 Feb 2023
- 2023A Review of the Trends and Challenges in Adopting Natural Language Processing Methods for Education Feedback Analysis20 Jan 2023
- 2022Deep-MDS Framework for Recovering the 3D Shape of 2D Landmarks from a Single Image27 Oct 2022
- 2022DPDR: A novel machine learning method for the Decision Process for Dimensionality Reduction17 Jun 2022
- 2021Unified Framework for Spectral Dimensionality Reduction, Maximum Variance Unfolding, and Kernel Learning By Semidefinite Programming: Tutorial and Survey29 Jun 2021
- 2021Reproducing Kernel Hilbert Space, Mercer's Theorem, Eigenfunctions, Nystr"om Method, and Use of Kernels in Machine Learning: Tutorial and Survey15 Jun 2021
- 2021Laplacian-Based Dimensionality Reduction Including Spectral Clustering, Laplacian Eigenmap, Locality Preserving Projection, Graph Embedding, and Diffusion Map: Tutorial and Survey3 Jun 2021
12 newest of 14 All 14 (JSON)