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

learn more… | top users | synonyms (1)

0
votes
1answer
36 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 ...
1
vote
1answer
17 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 ...
0
votes
0answers
4 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 ...
0
votes
0answers
13 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 ...
0
votes
0answers
20 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 ...
0
votes
0answers
11 views

how to normalize a ratio from 0 to 1, the 0,5 being defined by a fixed number [closed]

I calculate a ratio X. I have an alert ratio Y: if X is > to Y it's good, below it's bad I need to normalize the ratio X to z, as 0 < z < 0.5 if X < Y and 0.5 > z > 1 if X > Y. I've tried: ...
0
votes
1answer
19 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 = ...
0
votes
0answers
17 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 ...
1
vote
1answer
41 views

De Normalize data

How would I de normalize the values which where normalized by the min max normalization below ?
0
votes
2answers
20 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) = ...
0
votes
1answer
30 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 ...
1
vote
1answer
39 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 ...
0
votes
0answers
17 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 ...
0
votes
0answers
18 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 ...
1
vote
0answers
17 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 ...
0
votes
1answer
29 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: ...
0
votes
0answers
20 views

How do I do RLM normalization in R?

I have a combined dataset of 123 samples and 9,482 features (expression levels of antibodies from Invitrogen's ProtoArray v5.0). Based on ...
0
votes
0answers
11 views

Normalizing Concepts in an Ontology

Background I would like to develop a simple ontology (2-3 levels) for example: ...
1
vote
1answer
189 views

Normalizations: dividing by mean

I'm reading various papers and I don't understand the meaning of three types of normalizations used. Let's say I have the number of calls $X_i(t)$ in region $i$ at time $t$. I see it normalized with: ...
0
votes
0answers
10 views

Creating A weighted Scorecard using Stats

Im new to forum and have tried reading some articles to solve my problem but had trouble understanding some of it and others might not have been completely relevant. Below I will outline my problem ...
0
votes
1answer
25 views

NLP: How to do feature normalisation for gender classificiation?

NOTE Before I begin, this F-measure is not related to precision and recall, and its title and definition is taken from this paper. I have a feature known as the F-measure, which is used to measure ...
0
votes
0answers
18 views

Baseline normalization

Let's assume that I have data that is a 10x90 matrix (10 variables, 90 samples). 90 samples are divided into three groups: 30 - baseline 30 - treatment-1 30 - treatment-2 Only treatment samples ...
2
votes
1answer
77 views

How to handle data normalization in kNN when new test data is received

I had a discussion with my colleagues about the following problem: Lets say we have 100 points of labeled data and we are using $k$-nearest neighbor method for prediction. So our data looks like ...
1
vote
0answers
11 views

Mediation using normalized variables

If I normalize (range 0-1) variables x, m, y prior to conduct a mediation (OLS), do I report them as "fully normalized coefficients". How do I interpret the results? I am used to report unstandardized ...
0
votes
0answers
25 views

Normalizing bimodal distribution

I have two sets of student test data (Version 1 and Version 2). These tests were given to over 500 students. Both tests appear to have a bimodal distribution, and one test appears to be harder than ...
0
votes
1answer
42 views

Weekly data normalization - Python

I have a weekly dataset and I have to normalize this data. Data is something like this : ...
1
vote
0answers
17 views

(LDA) Topic Modeling: eliminate Junktopics through normalization

Question: Is it reasonable to normalize topics to eliminate junk-topics and get a better distinction of document-relations? I used the MALLET-LDA Java-library to estimate a ParallelTopicModel with ...
4
votes
1answer
66 views

normalization effect on polynomial regression

I need to know why input normalization has no effect on polynomial regression. Here there is a good explanation proving that column normalization does not affect linear regression. But I need to know ...
1
vote
1answer
42 views

Tsallis and Renyi Normalized Entropy

I'm working with Shannon, Tsallis and Renyi entropies. I need to normalized these entropies for comparison purposes. In Shannon's entropy you need only to divide by the log of the number of bins. ...
1
vote
0answers
54 views

High variability in data set, how to normalize? [closed]

For my research I'm doing FACS analysis on neutrophils isolated from venous blood. The blood is taken from different individuals each day so there is high variation in the results, resulting in a high ...
0
votes
0answers
26 views

removing batch effect when combing patient's data into a large cohort

I have some clinical data quantifying severity of disease for patients from 3 different hospitals. Basically, the patient severity vector for each hospital looks like below: ...
0
votes
0answers
19 views

How to normalize scoring to eliminate bias?

Background: I'm writing an app to allow multiple people to rate an object on a scale of 1-10. There are a few hundred objects to be rated, so I'd like to allow the reviewers to review what they can, ...
0
votes
0answers
18 views

what's your process for normalizing data?

I have a time series data set that I want to put through a simple linear regression. Besides guessing, what stat tests or processes can I use to normalize outliers?
2
votes
1answer
127 views

two-sample KS test: data becomes significantly different after normalization

I'm currently working on a data set with two sets of samples. The csv file of the data could be found here. I would like to use KS test to see if these two sets of samples are from different ...
2
votes
0answers
23 views

Normalizing regressors in logistic space

I have a bunch of sklearn sgd models that have beta coefficients in the logistic space. I want to see if these models cluster ...
1
vote
2answers
60 views

How to normalize data without an edge

Is there some calculation method which normalizes data (numbers) if I don't have minimum and maximum edge? Only one I know is: y = (x - min-x) / (max-x - min-x). But as I said I don't have an edge ...
1
vote
0answers
27 views

Time series comparisons: early detection of mismatching series after n points for efficiency

I am doing time series comparisons. I have a set of values (my query set Q) that I need to compare against many other reference sets (R), each of which contains the same number of values as my query ...
0
votes
0answers
16 views

continuous norming

I am said to apply continious norming to my data (test scores testing children). I have not found any freely available information about this process. I have found out that: 1) norming is based on ...
1
vote
1answer
35 views

Fix dominant columns/rows in symmetric data matrix?

I have a symmetric data matrix $A$, giving co-occurrence of events. That is, $A_{ij}$ is the frequency of occurrence of $i,j$ together. The diagonal elements of $A$ are unknown/indeterminate. I am ...
3
votes
1answer
48 views

Is there a way to characterize the position of a point in a distribution that takes higher moments into account?

When summarizing a location in a distribution, we can take the mean into account by simply calculating $x-\mu$. We can take the standard deviation into account by calculating a Z score ...
1
vote
0answers
18 views

How to normalize by the covariance matrix? [duplicate]

I am trying to understand an image processing research paper [1] that calls for normalizing a distance between an object's center point and the center of a cluster of points by the covariance matrix ...
0
votes
1answer
25 views

Normalization Interval variable [duplicate]

I have to perform a regression. My data set is composed of 12 independent(ration) variables and 1 dependent(interval) variable. After having performed a Normality test(kolmogorov-Smirnov) it resulted ...
1
vote
0answers
56 views

Data normalization for a recommender

this question is a copy from DataScience forum. I do hope I will get much more information and help here. At the moment I am doing some data experiments with the Graphlab toolkit. First of all, I ...
0
votes
0answers
52 views

When do we have to standardize/normalize data?

I have to perform an independent group t-test and have unequal variances. From what I understand, the solution is to transform the data. To transform it, do I standardize it? Or simple use log10 to ...
1
vote
0answers
46 views

How to normalize data in close range 0.95-1.05 to scale between 0.9 and 1? [closed]

everybody, I have a problem: I have data in close range (0.95 to 1.05 or -0.05 to to 0.05), but these values (presented as percentage in the end) can not be presented as negative numbers nor with ...
0
votes
1answer
80 views

Normalize variables for calculation of correlation coefficient

I have two vectors (arrays) of values. One vector represents a variable whose values are between 0 and 1 (ratio-type variable). The other vector represents a variable whose values are continuous float ...
0
votes
0answers
26 views

Normalisation formula applicable to one or more data items

I've created a recurrent neural network, to which normalised values are passed as inputs. The normalization formula is: $$\tilde{x_{i}} = \frac{1}{1+exp(-\frac{x_i-\bar{x}}{\sigma})},$$ where ...
0
votes
0answers
50 views

How to determine a data transformation factor

I'm working on transforming one set of data to another based on a certain variable (length). Here's how the actual problem is like: ...
0
votes
0answers
31 views

Normalization in case of clustering

Can normalization in this form be used (x−μ)/σ and should it be used in case of clustering? I have parameters on different scales and since I'm calculating the distances I need to perform feature ...
0
votes
1answer
30 views

Normalization problems - how to normalize in case of set of points while new points arriving

I'm having a procedure in which I perform clustering, and later, for each new example I test if that example belongs to some of existing clusters, by calculating distance to existing centroids. To ...