noise reduction in data analysis - 968 Words

NOISE REDUCTION IN DATA USING POLYNOMIAL REGRESSION Geetha Mary A, Dinesh Kumar P, Girish Kumar K, Gyanadeep N School of Computing Science and Engineering, VIT University dinesh.venkata@yahoo.co.in Abstract:-Noise is common in data which hinders the data analysis. We consider noise as low-level data errors or objects that are irrelevant to data analysis. Data cleaning technique reduces the low-level data errors but not irrelevant objects. To reduce both types of noise there are three traditional outlier detection techniques distance-based, clustering-based, and an approach based on the Local Outlier Factor (LOF) of an object. In this paper we introduce a new method for noise reduction using polynomial regression and spearman’s rank†¦show more content†¦Using the equations we get values for x and y. Now we apply spearman’s rank correlation coefficient Ï  for the obtained results. If Ï  Ï µ (-1,1) then the data is not noise, if the Ï  value is not in that range then we consider it as noise. 3. Numerical Evaluation Let us take data set as follows X Y 5 8 6 9 7 10 8 11 9 12 10 13 The equations obtained from the above data set are Y=0.990 * x ^ 1.000+ 3.931 X=0.989 * y ^ 1.000- 3.249 Now we take another data set to test using above equations and if the results are not approximately similar to the obtained results for all the models then we consider the data to be noise or outlier. For example we take the test data set to be as follows, X Y 112 125 167 171 For test case 1 (112,125) Taking x(112), then y=114.811 Taking y(125), then y=120.376 For test case 2(167,170) Taking x(167) ,Y=169.261 Taking y(170) ,X=166.87 For the obtained results we apply spearman’s rank correlation coefficient Ï , Ï  = 1-(6∑di2/n3-n) if the obtained result lies between (-1,1) then the data belongs to that set and if not in that range then it is considered as noise or outlier. By applying for the above results we get, For test case1 (112,125) Ï =1-((6*((114.811-125)2+(120.376-112)2))/(2*(4-1))) Ï  = -172.973 For test case2 (167,170) Ï =1-(((6*(167-166.87)2+(170-169.261)2))/(2*(4-1))) Ï  =0.436979 so from the obtained results test case1 is considered as noiseShow MoreRelatedDigital Image And Its Effect On The Quality Of Image1246 Words   |  5 Pages Abstract: In image processing, noise reduction and restoration of image is expected to improve the qualitative inspection of an image and the performance criteria of quantitative image analysis techniques Digital image is inclined to a variety of noise which affects the quality of image. The main purpose of de-noising the image is to restore the detail of original image as much as possible. The criteria of the noise removal problem depends on the noise type by which the image is corrupting Read MoreImage Processing Essay1065 Words   |  5 Pages4.1 INTRODUCTION In image processing, noise reduction and restoration of image is expected to enhance the qualitative inspection of an image and the performance criteria of quantitative image analysis methods Digital image is inclined to a variety of noise which attribute the quality of image. The main purpose of de-noising the image is to reinstate the detail of original image as much as possible. The criteria of the noise removal problem depends on the noise type by which the image is contaminatedRead MoreEvaluation Of The Performance Improvement1410 Words   |  6 Pagesbe an ongoing goal to guide daily processes (Shaw Elliott, 2012). Data collection is the foundation of all PI activities the type of data should first be identified, and then collected. 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Wavelet transformation method produces better lossless com pressionRead MoreTaguchi Method1514 Words   |  7 PagesMonterrey Design and Analysis of Experiments Research TAGUCHI METHOD Fernando de Castro Palomino Siller – A01191233 Thursday, September 17th, 2015 Professor: Hà ©ctor Rincà ³n CONTENT Introduction †¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦.†¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦ 3 Why to use Taguchi Method †¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦.†¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦. 3-4 Taguchi Method Strategy †¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦.†¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦ 4 P-diagram †¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦.†¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦.. 5 Quality Measurement †¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦.†¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦. 5-6 Signal To Noise (S/N) Ratios †¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Read MoreHyperspectral Image Classification1485 Words   |  6 PagesClassification is a principle technique in hyperspectral images (HSI) analysis, where a label is assigned to each pixel based on its characteristics. 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