A way of re-expressing data to make their values lie between 0 and 1 (or 0% and 100%).

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26 views

Is it possible (and if yes how) to retain a sparse matrix after normalization?

I was wondering whether given a sparse matrix it is possible to retain a sparse matrix after removing certain global effects. Let me demonstrate the following: Given a data set $X$ with dimensions $m ...
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15 views

data normalization [on hold]

I got some metabolomics peak intensity data recently. There are five time points in my experiments and 9 replicates for each point. The LC/MS detected hundreds of compounds, now I need to analyze ...
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15 views

Effect of number of cases in “sum to unity” normalization [on hold]

I am normalizing a set of numbers so that the add up to 1. In order to do this I just divide each number by the sum of all of the numbers in the set. I am doing this for several sets that don't have ...
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24 views

Normalize estimated probabilities from two logistic regression models

I have built two logistic regression models predicting the probability of purchase of two products. (Product A and Product B) For every customers, I want to choose the product that has the higher ...
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35 views

Does feature size affect polynomial regression?

(I'm still trying to learn all this, sorry for any wrong terms or mistakes I might have made in this question) By feature size, I mean the value of the numbers. For example, let's say I have input ...
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17 views

Normalizing test scores — public health inspections across diverse jurisdictions

I'm building a system to put the world's restaurant health scores online. I'm implementing a data standard which only requires the scores to be 0–100, 100=maximum. That was easy. My current problem ...
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61 views

Feature scaling and mean normalization

I'm taking Andrew Ng's machine learning course and was unable to get the answer to this question correct after several attempts. Kindly help solve this, though I've passed through the level. ...
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1answer
41 views

What is the best data transformation for absolute zero inflated distributions?

I have 3 variables with the following distributions: What is the most appropriate transformation to make them as normally distributed as possible? This data is absolute zero inflated.
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1answer
16 views

Normalising extreme items within datasets

I have a a dataset where each item is a % above or below 100% (taking an individual item, dividing by the mean). In order to produce a rank I weighted each item by a % (summing to 100%) to provide a ...
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1answer
79 views

Bayes theorem: normalisation denominator and likelihood

I have been racking my brains trying to understand Bayes theorem. So, the way I have understood is that the likelihood is the probability of observing the particular outcome given a set of parameter ...
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38 views

How to normalize mixed continuous/discrete features for DNN?

I have had some success training my deep neural network (with ReLU hidden units) by first normalizing the features of my data set to zero-mean-unit-variance. Each sample of my data set has 600+ ...
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31 views

For normalized X and Y, how can the slope be equal in lm(Y~X) and lm(X~Y)

Lets consider normalized variables X and Y. Slope of a lm(Y~X) is Cor(Y,X)*sd(Y)/sd(X) and for ...
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3answers
80 views

Andrew Ng scaling and normalizing expression

I'm involved in the design of a recommender system and I'm using a mixture expression of z-score and min-max scaling for scaling and normalizing data: $X_{norm} = \frac{X - \mu}{X_{max} - X_{min}}$ ...
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2answers
98 views

LASSO - normalization of response variable needed?

I wonder whether the response variable needs to be normalized before LASSO estimation (I am using the lars package in R to perform LASSO estimation). My guess is that only right-hand side variables ...
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1answer
102 views

How do you classify based on percentile ranking when most scores are the same?

I am dealing with a simple dataset of test scores. It was an easy test -- 98 out of 100 persons got a perfect score. 1 person got a 2% and one person got a 3%. Here's what it looks like in ...
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19 views

Should I normalize my variables for a descriptive logistic regression?

I'm running a logistic regression in order to descriptively analyze the relationship between my independent and dependent variables. As I understand it, it's not mathematically necessary to normalize ...
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23 views

Weather analysis | company sales

I'm writing a python code that reads in a csv file of rain in inches for a given zip code and creates a normal distribution from the data. Ultimately, I want to be able to create some score for the ...
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29 views

Normalize time series data - Wikipedia article counts

I have: 3 wikipedia article access counts (weekly) (A-B-C) Ground truth data (weekly) Total wikipedia english article traffic counts (weekly) My purpose is, build a multiple linear regression ...
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1answer
34 views

Should I normalize the data (capital stock series) after deflating it with whole price index? [closed]

I am working on across industries. I want to know that after deflation capital stock of large scale industries with whole sale price index, Is there need of normalizing the data series?
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2answers
134 views

What are the real benefits of normalization (scaling values between 0 and 1) in statistics?

I am working on student data set in which I want to normalize the range of percentage to 0,1. But I am not clear with the actual benefits of normalizing a range.
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36 views

Weighted Least Squares Normalization for Parameter Uncertainty

I want to fit a function $f(x_1,x_2..)$ to (noisy) data with unknown variance. For each datapoint, I have a weight $w_i$ which is proportional to the reliability of that particular datapoint. The real ...
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26 views

Data normalization of different data ranges in R with reduced data loss

I have a data.frame with columns that contains data in different ranges. For example, below is the max and min values for these columns: ...
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45 views

Using variance in place of standard deviation for z-normalization

I'm implementing a 1-nearest neighbor (with dynamic time warping as the distance measure) classification algorithm on a severely constrained embedded platform with no FPU, so we're doing fixed point ...
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22 views

Normalization of scale in cluster analysis

I have 16 variables which are scaled 1-5, 5 variable scaled 1-4 and 1 variable scaled 1-10. I suppose I will need to do normalization before applying cluster analysis. Variable response is in likert ...
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1answer
60 views

Normalizing a Continuous Variable for Appropriate Use Alongside Binary Variables

I am fitting a model where I estimate my Dependent Variable based on about 20 Binary Variables (0/1), and one continuous variable. I've read about the importance of normalizing that continuous ...
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21 views

Data transformation: normalizing the rows or the columns of a data matrix?

I am a bit confused on the kind of data transformation I have to use for analyzing my data. I have a matrix $X$, where rows are genes, columns are individuals, and entry $X_{i,j}$ is a value of ...
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43 views

Normality Problems [duplicate]

I'm doing a regression analysis which involves 4 independent variables (IV). I performed a Shapiro-Wilk test to test the normality of of each of the IVs and it turned out that the the test showed a ...
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1answer
29 views

How to remove normalization function on data? [closed]

I currently have my data scaled between the range of 0 and 1 using the following normalization function in R: ...
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1answer
40 views

Will normalizing training and testing data separately cause under/overfitting?

Suppose I have training and testing data and I want to train a classifier (e.g. SVM). Typically, features are normalized before classification to ensure some features aren't weighted more heavily than ...
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2answers
60 views

Kernel of a Normal Distribution

From Wikipedia , The kernel of a probability density function (pdf) or probability mass function (pmf) is the form of the pdf or pmf in which any factors that are not functions of any of the ...
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2answers
136 views

Is cosine similarity identical to l2-normalized euclidean distance?

Identical meaning, that it will produce identical results for a similarity ranking between a vector u and a set of vectors V. I have a vector space model which has distance measure (euclidean ...
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18 views

Why is the Multivariate RMSD “normalised” differently to the NMB?

For the multivariate case in regression, and also in other model predictions, the Root Mean Squared Deviation (RMSD or RMSE) is normalised by $n-p-1$, giving, $$RMSD = \sqrt{\frac{\sum_{i=1}^n ...
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33 views

Repeated measures ANCOVA

I have a data set with training effects and I measured the outcome variable before, during and after training. This is a repeated measures design and I hope to find an effect of training on the ...
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26 views

Normalized data and regression

Suppose I have eight subjects and measured performance in a time series (outcome measure is a distance measure). I assess learning effects across these time points by expressing the increase in ...
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29 views

Normalize(Scale) data before sampling or after sampling in binary classification?

I have a binary classification database with imbalance outputs (1 labeled data: 1400 samples, 0 labeled data: 200 samples). I balance data based on a criteria to (200 - 200). Where should I normalize ...
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1answer
61 views

Data normalization choice [duplicate]

What are the main advantages and disadvantages of normalization between 0 and 1 or the other zero mean variance one algorithm? If we want to preprocess the data, how to select either of these two ...
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1answer
29 views

Normalizing data before estimation of Granger causality?

I want to estimate granger causality between two series. Visual inspection indicates it might be useful to normalize data first (i.e. (X-mean(x))/ (sample stdev(x)) ) Are there some caveats ...
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11 views

Combining and normalizing gridded parameters of different distributions with the same units

I have a grid that has two parameters for each cell. One parameter, let's call it K, is the same for all grid cells. The other parameter, let's call it M, has values of M, 2M, and 3.5M, with each ...
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41 views

Standardization (z-score) across the “Samples” or across the “variables”?

I found in literature that one of the most common way of standardization data is to compute z-scores (mean subtraction and division by standard deviation). Can anybody tell me if it is ok to compute ...
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43 views

normalizing data for neural network

I'm working on a neural network with back propagation for indoor localization. The input of the neural network is Received Signal Strengths (RSSs) and the output is a coordinate (x,y). I have ...
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1answer
30 views

RMSE normalisation, what method prefer?

According to this article on wikipedia http://en.wikipedia.org/wiki/Root-mean-square_deviation, two methods are widely used to normalise the RMSE. The first is dividing by the range: $$NRMSE = ...
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37 views

Z-score across the “Samples” or across the “variables”?

I found in literature that one of the most common way of standardization data is to compute z-scores (mean subtraction and division by standard deviation). Can anybody tell me if it is ok to compute ...
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1answer
50 views

De Normalize data

How would I de normalize the values which where normalized by the min max normalization below ?
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2answers
43 views

Data normalization for RBF kernel

I have a matrix of values where rows are individuals and columns are attributes. I want to extract a similarity value for every pair of individuals, and I use an rbf kernel: $$k(x_i,x_j) = ...
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1answer
59 views

Some issues with standardized variable in a regression analysis

I'm solving this multiple choice question on the properties of a standardized variable. Two of the possible options (which are wrong but look right to me) are 1. It is always normally distributed and ...
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1answer
48 views

What's the best approach for results of a running race?

I am a student in a good statistics program, but I'm not always the best at picking the tools/process to apply to a problem. To be clear, this is NOT homework, I am asking for a project that I have in ...
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19 views

normalization of data points in order to make them adhere to a specified probability distribution (e.g. Normal)

I am doing some preprocessing for a computer vision task. My target is to select a few elements (pixels) containing highest scores according to a metric that I am developing. The values of this metric ...
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37 views

Normalizing interview scores

Our school debate team is interviewing potential new members. There are a lot of applicants, and so we have decided to only have six of us present at any given interview. We are judging each of the ...
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28 views

Does centering or mean normalizaiton alone every help in feature scaling?

In feature scaling, one way is to subtract the mean (centering) and then divide by the standard deviation for all data points. Suppose we just centered the data and didn't divide by the standard ...
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1answer
114 views

Feature normalization in Text Classification

I'm doing Text Classification in R, and my initial features are just word frequency inside a Document. For example: ...