Subspace Learning and Imputation for Streaming Big Data Matrices and Tensors - 2015


Extracting latent low-dimensional structure from high-dimensional information is of paramount importance in timely inference tasks encountered with “Huge Information” analytics. However, increasingly noisy, heterogeneous, and incomplete datasets, and the requirement for real-time processing of streaming information, create major challenges to the current end. In this context, the present paper permeates edges from rank minimization to scalable imputation of missing data, via tracking low-dimensional subspaces and unraveling latent (probably multi-means) structure from incomplete streaming information. For low-rank matrix knowledge, a subspace estimator is proposed primarily based on an exponentially weighted least-squares criterion regularized with the nuclear norm. After recasting the nonseparable nuclear norm into a kind amenable to online optimization, real-time algorithms with complementary strengths are developed, and their convergence is established under simplifying technical assumptions. During a stationary setting, the asymptotic estimates obtained supply the well-documented performance guarantees of the batch nuclear-norm regularized estimator. Underneath the same unifying framework, a unique online (adaptive) algorithm is developed to get multi-manner decompositions of low-rank tensors with missing entries and perform imputation as a byproduct. Simulated tests with each artificial plus real Internet and cardiac magnetic resonance imagery (MRI) information confirm the efficacy of the proposed algorithms, and their superior performance relative to state-of-the-art alternatives.

Did you like this research project?

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

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
PROJECT TITLE : Online Subspace Learning from Gradient Orientations for Robust Image Alignment ABSTRACT: Robust and effective picture alignment remains a difficult task due to the size and complexity of images as well as fluctuations
PROJECT TITLE :Subspace Rejection for Matching Pursuit in the Presence of Unresolved Targets - 2018ABSTRACT:Unresolved scatterers (separated by but a three-dB matched filter main lobe width) are known to degrade the matching pursuit
PROJECT TITLE :Large-Scale Kernel-Based Feature Extraction via Low-Rank Subspace Tracking on a Budget - 2018ABSTRACT:Kernel-primarily based ways get pleasure from powerful generalization capabilities in learning a selection of
PROJECT TITLE :Unified Discriminative and Coherent Semi-Supervised Subspace Clustering - 2018ABSTRACT:The ubiquitous large, complex, and high dimensional datasets in computer vision and machine learning generates the matter of

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

Project Enquiry