ABSTRACT:

Discriminant feature extraction plays a central role in pattern recognition and classification. Linear Discriminant Analysis (LDA) is a traditional algorithm for supervised feature extraction. Recently, unlabeled information have been utilised to enhance LDA. However, the intrinsic problems of LDA still exist and solely the similarity among the unlabeled data is utilised. In this paper, we have a tendency to propose a novel algorithm, known as Semisupervised Semi-Riemannian Metric Map (S3RMM), following the geometric framework of semi Riemannian manifolds. S3RMM maximizes the discrepancy of the separability and similarity measures of scatters formulated by using semi-Riemannian metric tensors. The metric tensor of each sample is learned via semisupervised regression. Our method will also be a general framework for proposing new semisupervised algorithms, utilizing the present discrepancy-criterion-based mostly algorithms. The experiments demonstrated on faces and handwritten digits show that S3RMM is promising for semisupervised feature extraction.


Did you like this research project?

To get this research project Guidelines, Training and Code... Click Here


MTechProjects.com offering final year Python Based Machine Learning MTech Projects, Machine Learning IEEE Projects, IEEE Machine Learning Projects, Machine Learning MS Projects, Python Based Machine Learning BTech Projects, Machine
PROJECT TITLE :A Machine Learning Approach for Tracking and Predicting Student Performance in Degree Programs - 2018ABSTRACT:Accurately predicting students' future performance based on their ongoing academic records is crucial
PROJECT TITLE :Optimal Bayesian Transfer Learning - 2018ABSTRACT:Transfer learning has recently attracted important research attention, because it simultaneously learns from different supply domains, that have plenty of labeled
PROJECT TITLE :Learning Graphs With Monotone Topology Properties and Multiple Connected Components - 2018ABSTRACT:Recent papers have formulated the problem of learning graphs from information as an inverse covariance estimation
PROJECT TITLE :Alternative to Extended Block Sparse Bayesian Learning and Its Relation to Pattern-Coupled Sparse Bayesian Learning - 2018ABSTRACT:We tend to consider the matter of recovering block sparse signals with unknown block

Ready to Complete Your Academic MTech Project Work In Affordable Price ?

Project Enquiry