Dimension Reduction using Orthogonal Local Preserving Projection in Big data

Abstract

Big Data is unstructured data that overcome the processing complexity of conventional database systems. The dimensionality reduction approach, which is a fundamental technique for the large-scale dataprocessing, try to maintain the performance of the classifier while reduce the number of required features. The pedestrian data includes a number of features compare to the other data, so pedestrian detection is the complex task. The accuracy of detection and location directly affect the performance of the entire system. Moreover, the pedestrian based approaches mainly suffer from huge training samples and increase the computation complexity. In this paper, an efficient dimensionality reduction model and pedestrian data classification approach has been proposed. The proposed model has three steps Histogram of Oriented Gradients (HOG) descriptor used for feature extraction, Orthogonal Locality Preserving Projection (OLPP) approach for feature dimensionality reduction. Finally, the relevant features are forwarded to the Support Vector Machine (SVM) to classify the pedestrian data and non-pedestrian data. The proposed HOG+OLPP+SVM model performance was measured using evaluation metrics such as precision, accuracy, recall and f-measure. The proposed model used the Penn-Fudan Database and compare to the existing research the proposed model improved approximately 6% of pedestrian data classification accuracy.

Study of the Operation Process of the E-Commerce Oriented Ecosystem of 5Ge Base Station, Which Supports the Functioning of Independent Virtual Network Segments

“…where U s (r s ) = ln(r s ) is the utility function of the parameter of the speed of information interaction for the s-th virtual network segment. To solve such a nonlinear optimization task, we apply the approximate gradient projection method [41,42], in which the matrix of projection on the hyperplane ( 5) is generally defined as…”