Adaptive Local Embedding Learning for Semi-supervised Dimensionality Reduction


In recent years, a lot of attention has been focused on semi-supervised learning, which is recognized as one of the most appealing problems in the Machine Learning research field. In this paper, we propose a novel locality preserved dimensionality reduction framework. We call it Semi-supervised Adaptive Local Embedding learning (SALE), and it learns a local discriminative embedding by constructing a k1 Nearest Neighbors ( k1 NN ) graph on labeled data. This is done in order to explore the intrinsic structure, also known as sub-manifolds, that are present in non-Gaussian labeled data. The next step is to map all of the samples into the learned embedding and build a new k2 NN graph on all of the embedded data in order to investigate the overall structure of all of the samples. Therefore, the unlabeled data and their corresponding labeled neighbors can be clustered into the same sub-manifold to improve the discriminative power of embedded data. This is done in order to maximize the information gained from the embedded data. In addition, based on the proposed SALE framework, we present two semi-supervised dimensionality reduction methods with orthogonal and whitening constraints. In order to solve the NP-hard problem that is present in our models, an effective alternatively iterative optimization algorithm has been developed. Our methods have been shown to be superior for the exploration and classification of local structures, as demonstrated by extensive experiments carried out on a variety of synthetic and actual world data sets.

Did you like this research project?

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

PROJECT TITLE :A Holistic Approach for Distributed Dimensionality Reduction of Big Data - 2018ABSTRACT:With the exponential growth of knowledge volume, huge information have placed an unprecedented burden on current computing
PROJECT TITLE :Face recognition using supervised probabilistic principal component analysis mixture model in dimensionality reduction without loss frameworkABSTRACT:During this study, 1st a supervised version for probabilistic
PROJECT TITLE :Probing Projections: Interaction Techniques for Interpreting Arrangements and Errors of Dimensionality ReductionsABSTRACT:We introduce a group of integrated interaction techniques to interpret and interrogate dimensionality-reduced
PROJECT TITLE :What Strikes the Strings of Your Heart?–Multi-Label Dimensionality Reduction for Music Emotion Analysis via Brain ImagingABSTRACT:After twenty years of in depth study in psychology, some musical factors are identified
PROJECT TITLE : Video Dissemination over Hybrid Cellular and Ad Hoc Networks - 2014 ABSTRACT: We study the problem of disseminating videos to mobile users by using a hybrid cellular and ad hoc network. In particular, we formulate

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

Project Enquiry