A Novel Multiple Kernel Learning Framework for Heterogeneous Feature Fusion and Variable Selection


We propose a novel multiple kernel learning (MKL) algorithm with a group lasso regularizer, called group lasso regularized MKL (GL-MKL), for heterogeneous feature fusion and variable selection. For problems of feature fusion, assigning a group of base kernels for each feature type in an MKL framework provides a robust way in fitting data extracted from different feature domains. Adding a mixed $ell _{1,2}$ norm constraint (i.e., group lasso) as the regularizer, we can enforce the sparsity at the group/feature level and automatically learn a compact feature set for recognition purposes. More precisely, our GL-MKL determines the optimal base kernels, including the associated weights and kernel parameters, and results in improved recognition performance. Besides, our GL-MKL can also be extended to address heterogeneous variable selection problems. For such problems, we aim to select a compact set of variables (i.e., feature attributes) for comparable or improved performance. Our proposed method does not need to exhaustively search for the entire variable space like prior sequential-based variable selection methods did, and we do not require any prior knowledge on the optimal size of the variable subset either. To verify the effectiveness and robustness of our GL-MKL, we conduct experiments on video and image datasets for heterogeneous feature fusion, and perform variable selection on various UCI datasets.

Did you like this research project?

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

PROJECT TITLE :A novel maximum power point tracking technique based on fuzzy logic for photovoltaic systemsABSTRACT:Most power purpose tracking (MPPT) techniques are thought of a crucial part in photovoltaic system design to
PROJECT TITLE :SCDN: A Novel Software-Driven CDN for Better Content Pricing and Caching - 2018ABSTRACT:In conventional content delivery networks (CDNs), where the content providers (CPs) are lack of the topology information, it
PROJECT TITLE :LAW: A Novel Mechanism for Addressing Hidden Terminal Problem in LTE-U and Wi-Fi Networks - 2018ABSTRACT:Recently, the use of LTE in unlicensed spectrum (LTE-U) has gained a lot of attention. One of the daunting
PROJECT TITLE :Hybrid Phased-MIMO Radar: A Novel Approach With Optimal Performance Under Electronic Countermeasures - 2018ABSTRACT:This letter presents a completely unique technique for optimizing the performance of a phased-multi-in
PROJECT TITLE :A Novel Power Minimization Precoding Scheme for MIMO-NOMA Uplink Systems - 2018ABSTRACT:This letter is to investigate the employment of non-orthogonal multiple access (NOMA) and new transceiver design for multiple-input

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

Project Enquiry