Project Categories

 
Matlab Projects in Bnaglore|VLSI Projects in Bangalore|ECE Projects in Bangalore|EEE Projects in bangalore,Mtech internship,matlab project centers in bangalore
Mtech internship,Power System Projects,Arduino Projects,IEEE ECE Projects,Raspberry pi Projects,VHDL Projects,SIMULINK Projects,MATLAB Projects call:9591912372 Download Our Android App Mtech Projects,Mtech Matlab Projects in Banglore,Mtech VLSI Projects in Bangalore,Mtech IEEE Projects,Mtech internship,matlab project centers in bangalore IEEE Matlab Projects in Bnaglore,IEEE VLSI Projects in Bangalore,2024 IEEE Project List,2024 VLSI Project List,2024 IEEE Matlab Basepaper,matlab project centers in bangalore

Joint Low-Rank and Sparse Principal Feature Coding for Enhanced Robust Representation and Visual Classification

Abstract

Recovering low-rank and sparse subspaces jointly for enhanced robust representation and classification is discussed. Technically, we first propose a transductive low-rank and sparse principal feature coding (LSPFC) formulation that decomposes given data into a component part that encodes low-rank sparse principal features and a noise-fitting error part. To well handle the outside data, we then present an inductive LSPFC (I-LSPFC). I-LSPFC incorporates embedded low-rank and sparse principal features by a projection into one problem for direct minimization, so that the projection can effectively map both inside and outside data into the underlying subspaces to learn more powerful and informative features for representation. To ensure that the learned features by I-LSPFC are optimal for classification, we further combine the classification error with the feature coding error to form a unified model, discriminative LSPFC (D-LSPFC), to boost performance. The model of D-LSPFC seamlessly integrates feature coding and discriminative classification, so the representation and classification powers can be enhanced. The proposed approaches are more general, and several recent existing low-rank or sparse coding algorithms can be embedded into our problems as special cases. Visual and numerical results demonstrate the effectiveness of our methods for representation and classification.

Joint Low-Rank and Sparse Principal Feature Coding for Enhanced Robust Representation and Visual Classification pdf

 

Projects at Bangalore offers Final Year students Engineering projects - ME projects,M.Tech projects,BE Projects,B.Tech Projects, Diploma Projects,Electronics Projects,ECE Projects,EEE Projects,Mechanical projects,Bio-Medical Projects,Telecommunication Projects,Instrumentation Projects,Software Projects - MCA Projects,M.Sc Projects,BCA Projects,B.Sc Projects,Science Exhibition Kits,Seminars,Presentations,Reports and Power System Projects,Arduino Projects,IEEE ECE Projects,Raspberry pi Projects,VHDL Projects,SIMULINK Projects,MATLAB Projects,Mtech internship etc


Facebook Twitter MTech Projects

Joint Low-Rank and Sparse Principal Feature Coding for Enhanced Robust Representation and Visual Classification,Optimal solutions for Sparse Principal Component Analysis,Streaming Sparse Principal Component Analysis,Large-Scale Sparse Principal Component Analysis,Structured Sparse Principal Component Analysis,Visual Classification

MTech Projects