Research ArticleOpen accessIn the keywordsAsymptotic Properties of Local Sampling on ManifoldYury Aleksandrovich YanovichIn many applications, the real high-dimensional data occupy only a very small part in the high dimensional ‘observation space’ whose intrinsic dimension is small. The most popular model of such data is Manifold model which assumes that the data lie on or near an unknown manifold Data Manifold, (DM) of lower dimensionality embedded in an ambient high-dimensional input space (Manifold … …Journal of Mathematics and Statistics, Vol. 12, Issue 3 · 26 Aug 2016 · 7,698 accessesView PDFCite
Research ArticleOpen accessIn the keywordsFace Recognition Based on Nonlinear Feature ApproachEimad E.A. Abusham, Andrew T.B. Jin, Wong E. Kiong, G. DebashisFeature extraction techniques are widely used to reduce the complexity high dimensional data. Nonlinear feature extraction via Locally Linear Embedding (LLE) has attracted much attention due to their high performance. In this paper, we proposed a novel approach for face recognition to address the challenging task of recognition using integration of nonlinear dimensional reduction Locally Linear Embedding integrated with Local … …American Journal of Applied Sciences, Vol. 5, Issue 5 · 31 May 2008 · 6,520 accessesView PDFCite
Review ArticleOpen accessIn the abstractMachine Learning Integration for Precise Facial Micro-Expression RecognitionViola Bakiasi Shtino, Markela Muça, Senada BushatiThis study advances facial micro-expression recognition through innovative machine-learning techniques, addressing critical needs in psychology, security, and human-computer interaction. The purpose of this study is to improve micro-expression recognition through the optimization of feature transformation and machine learning algorithms. We introduce a novel approach combining Kernel Principal Component Analysis (KPCA) and Uniform Manifold Approximation and Projection (UMAP) for dimensionality reduction, … …Journal of Computer Science, Vol. 20, Issue 11 · 22 Oct 2024 · 5,592 accessesView PDFCite