Refining Face Recognition: Maximizing Performance with Dimensionality Reduction and Ensemble Learning in K-Nearest Neighbors

Author(s)	Chethan TS, Abheesh Puthukkudy
Country	India
Abstract	In the field of image classification, the K-Nearest Neighbors (KNN) algorithm is favored for its simplicity and effectiveness. However, the high dimensionality of image data often challenges KNN’s performance. This study investigates the impact of three dimensionality reduction techniques—Principal Component Analysis (PCA), Uniform Manifold Approximation and Projection (UMAP), and Linear Discriminant Analysis (LDA)—on enhancing KNN’s accuracy and efficiency in image classification, where KNN and Bagging classifier is computed without using any library but only using mathematical formulation. Additionally, the study examines the effect of ensemble learning, specifically through bagging, on KNN's performance.
Keywords	K-Nearest Neighbors, Image Classification, PCA, UMAP, LDA, Bagging, Dimensionality Reduction, Machine Learning
Field	Computer > Artificial Intelligence / Simulation / Virtual Reality
Published In	Volume 6, Issue 3, May-June 2024
Published On	2024-06-01
Cite This	Refining Face Recognition: Maximizing Performance with Dimensionality Reduction and Ensemble Learning in K-Nearest Neighbors - Chethan TS, Abheesh Puthukkudy - IJFMR Volume 6, Issue 3, May-June 2024. DOI 10.36948/ijfmr.2024.v06i03.21799
DOI	https://doi.org/10.36948/ijfmr.2024.v06i03.21799
Short DOI	https://doi.org/gtxrp4

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