Dimension reduction using orthogonal local preserving projection in big data

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 data-processing, 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.