PROJECT TITLE :

A General Approach for Achieving Supervised Subspace Learning in Sparse Representation

ABSTRACT:

A vast family of subspace learning algorithms based on dictionary learning has been developed during the last few decades to give alternative solutions for learning subspace features. The majority of them are unsupervised algorithms that are used on data with no labels. It's worth mentioning that label information is accessible in some applications, such as facial recognition, where the dimensionality reduction approaches stated above can't use it to increase their performance. To increase performance in certain labeled cases, an unsupervised subspace learning method must be transformed into the appropriate supervised approach. We offer a technique in this research that may be utilized as a general method for constructing a supervised algorithm based on any unsupervised subspace learning algorithm that uses sparse representation. We also create a new supervised subspace learning algorithm called supervised principal coefficients embedding using the proposed method (SPCE). We demonstrate that SPCE outperforms the state-of-the-art supervised subspace learning algorithm.


Did you like this research project?

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


PROJECT TITLE : Flipping Free Conditions and Their Application in Sparse Network Localization ABSTRACT: An essential challenge involves determining the topology of a network based on the distances between its nodes. When there
PROJECT TITLE : Foresee Urban Sparse Traffic Accidents: A Spatiotemporal Multi-Granularity Perspective ABSTRACT: Due to the rapid pace of urbanization, car accidents have evolved into a significant threat to both health and development.
PROJECT TITLE : GraphSAGE-Based Traffic Speed Forecasting for Segment Network With Sparse Data ABSTRACT: The ability to accurately anticipate the flow of traffic is an essential component of intelligent traffic management systems.
PROJECT TITLE : Robust Rank-Constrained Sparse Learning: A Graph-Based Framework for Single View and Multiview Clustering ABSTRACT: Graph-based clustering is an approach that seeks to partition data in accordance with a similarity
PROJECT TITLE : Regularization on Augmented Data to Diversify Sparse Representation for Robust Image Classification ABSTRACT: The process of image classification is an essential part of today's computer vision systems. Due to

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

Project Enquiry