Linear Regression Classification Algorithm Research Articles

Diagnostic on Car Internal Combustion Engine through Noise

Internal Combustion Engines are known for their unique sound characteristics. Through these sound characteristics, an experienced car mechanic will be able to diagnose the type of engine damage just by listening to the sound. This reduces the need to disassemble components to pinpoint machine faults which also contributes to a significant reduction in overall repair time. The main aim of this paper is to build a process to identify faulty machines through engine noise analysis with visual data to determine machine faults at an early stage. By capturing various types of engine sounds, data visualization uses healthy engine sounds and broken engine sounds obtained from cars as well as various types of broken engine sounds that are usually found in vehicles. This audio data will be used in audio signal processing combined with a linear regression classification algorithm. Visualization data can distinguish various types of sounds that are commonly found in damaged or damaged engines such as clicks, ticks, knocks and other types of sounds to determine the types of damage that are usually found in internal combustion engines. The data used comes from Kaggle, which is public data which is widely used as general data for data science activities. Visually, data from vehicle engines can be seen from the data on which car brand is the best in terms of sound. The results using linear regression show the R-squared score (R^2) or also called the coefficient of determination reaching 91.95%.

Recognition of Continuous Face Occlusion Based on Block Permutation by Using Linear Regression Classification

Face occlusion is still a key issue in the study of face recognition. Continuous occlusion affects the overall features and contour structure of a face, which brings significant challenges to face recognition. In previous studies, although the Representation-Based Classification Method (RBCM) can better capture the differences in different categories of faces and accurately identify human face images with changes in light and facial expressions, it is easily affected by continuous occlusion. For face recognition, there is a situation where face error recognition occurs. The RBCM method frequently learns to cover the characteristics of face recognition and then handle face error recognition. Therefore, the elimination of occlusion information from the image is necessary to improve the robustness of such models. The Block Permutation Linear Regression Classification (BPLRC) method proposed in this paper includes image block permutation and Linear Regression Classification (LRC). The LRC algorithm belongs to the category of nearest subspace classification and uses the Euclidean distance as a metric to classify images. The LRC algorithm is based on one of the classification methods that is susceptible to outliers. Therefore, block permutation was used with the aim of establishing an image set that does not contain much occlusion information and constructing a robust linear regression model. The BPLRC method first modulates all the images and then lists the schemes that arrange all segments, enters the image features of various schemes into linear models, and classifies the result according to the minimum residual of the person’s face image and reconstruction image. Compared to several state-of-the-art algorithms, the proposed method effectively solves the continuous occlusion problem for the Extended Yale B, ORL, and AR datasets. The proposed method recognizes the AR data concentration scarf to cover the accuracy of human face images to 93.67%. The dataset recognition speed is 0.094 s/piece. The arrangement method can be combined not only with the LRC algorithm, but also other algorithms with weak robustness. Due to the increase in the number of blocks and the increase in the calculation index of block arrangement methods, it is necessary to explore reasonable iteration methods in the future, quickly find the optimal or sub-best arrangement scheme, and reduce the calculation of the proposed method.

Linear Regression Classification in the Quaternion and Reduced Biquaternion Domains

Linear regression classification (LRC) has proven to be a successful recognition tool in recent years. LRC depends on using the least square algorithm to get the solution of the linear regression equation. To improve the performance of the LRC algorithm, in this paper, we extend the LRC strategy to both quaternion and reduced biquaternion domains to consider image color information. We derive closed-form solutions from the properties of both domains<i>.</i> We also improve on the accuracy of the closed-form solutions using nonlinear optimization. Our experiments on three benchmark color face recognition databases demonstrate the effectiveness of the proposed methods for recognizing color faces.

Linear regression classification steered discriminative projection for dimension reduction

Because of the simplicity and effectiveness of linear regression classification (LRC), LRC is widely applied into image classification. However, it processes the original high-dimensional data directly. It is well known that the original data usually contains a lot of redundant information or noise, which will reduce the performance of LRC algorithm and increase its running cost. At the same time, it usually suffers from out of sample problem. In order to overcome the weaknesses of LRC, a novel dimension reduction algorithm termed linear regression classification steered discriminative projection (LRC-DP) is presented by combining LRC with discriminative projection. LRC-DP not only fits LRC well, but also seeks a linear projection, in which the ratio of between-class reconstruction errors to within-class reconstruction errors is maximized in the transformation space. The proposed LRC-DP can learn a robust low-dimensional projection subspace from the original sample images in high-dimension space. In order to validate the performance of LRC-DP algorithm, extensive experiments are conducted on several public image databases. Experimental results reveal that the LRC-DP algorithm is feasible and effective.

Improved principal component analysis and linear regression classification for face recognition

In this paper, an improved principal component analysis (IPCA) is presented for face feature representation. IPCA is mainly designed to extract the useful information from original face images through reducing the dimension of feature vectors. Linear regression classification (LRC) algorithm is employed to treat the face recognition as a linear regression issue. LRC uses the least-square method to decide the class label with the minimum reconstruction error. Experiments are conducted on the Yale B, CMU_PIE and JAFFE databases. The proposed IPCA algorithm and LRC algorithm achieve better recognition results than that of state-of-the-art algorithms.

MapReduce-Based Parallel Linear Regression for Face Recognition

In order to solve the problem of high time complexity of Linear Regression Classification algorithm,we propose a Mapreduce-based parallel linear regression classification algorithm. The map task uses the test image vector and the vector subspace to predict the response vector for one class, then calculates the distance measure between the predicted response vectory and the original response vector. The reduce task processes the data which are generated by the mappers, the test image is assigned to the nearest class. The experiments shows that the MapReduce-based parallel linear regression classifier can significantly improve the efficiency of Face Recognition.