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

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15 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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11 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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15 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
22 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: ...
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14 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 ...
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11 views

Normalizing Concepts in an Ontology

Background I would like to develop a simple ontology (2-3 levels) for example: ...
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1answer
182 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: ...
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9 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 ...
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1answer
22 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 ...
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13 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 ...
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24 views

Normalisation constant for exponential distribution

I'm currently implementing this algorithm in Java for my university project (http://www.dcs.warwick.ac.uk/~nathan/resources/Publications/recsys-2011.pdf) and there's a part in section 4.1 on page 5 ...
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1answer
56 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 ...
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10 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 ...
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19 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 ...
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1answer
32 views

Weekly data normalization - Python

I have a weekly dataset and I have to normalize this data. Data is something like this : ...
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14 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 ...
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1answer
59 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 ...
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1answer
35 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. ...
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46 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 ...
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19 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: ...
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14 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, ...
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17 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?
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1answer
99 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 ...
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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 ...
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2answers
56 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 ...
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23 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 ...
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11 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 ...
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1answer
33 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 ...
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1answer
42 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 ...
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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 ...
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1answer
23 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 ...
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51 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 ...
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44 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 ...
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34 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 ...
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1answer
64 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 ...
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22 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 ...
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47 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: ...
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30 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 ...
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1answer
27 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 ...
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80 views

How to normalize the data to [0, 1] in R with data similar to χ²-distribution without shrinking lower values too much?

I want to normalize the data to [0,1], but the distribution of this array is quite not regular, having large quantity of low values and small quantity of large values, almost 80% values of data are in ...
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1answer
38 views

Noise removal from a dataset with a know distribution

If I have a dataset where it's points are drawn from a known distribution(For example a normal distribution) due to some noise the histogram doesn't reflect such a behavior (not necessarily skewed) ...
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2answers
65 views

Why do we whiten the data before running ICA?

Why do we usually pre-whiten the data before doing independent components analysis (ICA), like making all eigenvalues equal? Doesn't that take away some information regarding the data?
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20 views

r-pnn, normalization and different distance measures for each variable

Since pnn is a NN that uses a Radial kernel to classify data, I think the distance measure is key and, in consequence, the normalization of the data. Am I right? How does pnn package calculate the ...
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1answer
62 views

When normalization is counter-productive [duplicate]

Could you give me general examples of when normalization is not used properly and affects badly the classification accuracy, or when it is not needed?
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12 views

System failure data - normalize data when there is more data available for that system

Suppose I have the following data from 6 systems. The number of Type A systems is 2 in 6 so 1/3. There are 2/3 of B systems. In the 6 systems there were 40 failures in total. ...
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3answers
307 views

When should I apply feature scaling for my data

I started a discussion with a collague of mine and we started to wonder, when should one apply feature normalization / scaling to the data? Lets say that we have a set of features with some of the ...
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19 views

Which type of feature scaling to use [duplicate]

I've been writing some simple machine learning algorithms and looking at time-series data. In doing so, I have come across the use of feature scaling: rescaling and standardizing as they are referred ...
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1answer
49 views

Question in Artificial neural network normalization

I wish to normalize inputs parameters into [0 - 1] and fit into a neural network for training. I have done a simple normalization method - MinMax Question 1: if my inputs have negative values, do i ...
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1answer
44 views

Cumulative standard deviation of variables with different distribution

Suppose we have a set of basketball players, each of which have 9 associated performance categories. Each of these categories has a different distribution. I want to find a good way to represent how ...
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1answer
29 views

Symbol to indicate normalized or standardized variables

Is there a symbol to indicate that variables have been standardized? For example, if I have 2 different scoring functions Score1 and Score2. Let's say I want to form a combo score and show that the ...