Technique that renders observed or computed (dis)similarities among objects into distances in a low-dimensional space (usually Euclidean). It thus constructs dimensions for the data; the objects can be plotted and conceptualized in those dimensions

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3
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1answer
39 views

Using metric MDS with non-metric distances and assessing the fit quality

I'm going to perform MDS by means of cmdscale function of standard R library. I spent several hours googling it and finally have ...
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2answers
51 views

Do we have to scale new unseen feature data for prediction

In machine learning most algorithms require some kind of scaling to decrease error. This is my code: ...
5
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1answer
54 views

Finding optimal correspondences between objects given two square distance matrices

I would like to find the optimal correspondences between two systems of objects based on the distances between objects WITHIN the two systems. So, the input to the algorithm would be two square ...
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0answers
21 views

ordination of non gaussian data

I'm trying to ordinate a quite big dataset (44 variables with scaled values between 0 and 1, with 800 observations), with evident correlations between them (both spearman and pearson pairwise r ...
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1answer
27 views

Detect outliers in multi dimenstions

I tried to implement outlier detection for one dimension using inter quartile. for instance , a given variable cost or revenue or profit. but I'm missing outliers in other dimensions when running for ...
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0answers
24 views

How can I scale the $k$-th moment of a time series to a different time frequency?

I have a time series, let's say N daily log-returns. I want to study the moments (possibly the distribution) of the weekly returns. I have two ways: 1) Using the time-additivity property of ...
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0answers
27 views

Does it makes sense to perform MDS when $n<p$?

According to the reference quoted below, when performing a classical MDS on a dataset, I have to compute a centered matrix $B$ based on the dissimilarity matrix and then to compute the ...
0
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1answer
37 views

randomForest MDSplot help R

I am new to R and randomForests so bear with me. I am trying to visualise my randomForest a little better using the MDSplot() function in Random Forest. There are two things i would like to do, and i ...
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0answers
40 views

How could a hyperparameter grid search be visualised?

Consider a hyperparameter grid search that looks at the training and testing scores of an estimator with respect to multiple parameters like training epochs, number of nodes in layer 1, number of ...
3
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1answer
183 views

Input Normalisation for ReLU neurons

According to LeCun (1998) it is good practice to normalise all inputs so that they are centred around 0 and lie within the range of the maximum second derivative. So for example we would use ...
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0answers
21 views

how to calculate the variance contribution of individual coordinates for multidimensional scaling analysis

I have searched the CrossValidated and stak Overflow and found the following related threads, Standard method for calculating contribution of individual variable to outcome Non-metric Stress in 3 ...
0
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1answer
31 views

How to recursively normalize my histogram count

I need to scale my histogram as I cannot store them with large numbers. Hence, I need to normalize it. I have taken normalization factor as sum of total population. But this need to happen ...
3
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1answer
36 views

Multidimensional Unfolding permutation test with smacof package - What does p.value mean here?

I have a dissimilarity data matrix with 7 rows and 7 columns (two ways). Having done unfolding on it, I wanted to evaluate the observed stress value. Using permutation test with 1000 replication, the ...
2
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0answers
24 views

How to determine the number of random initializations to use in non-metric multidimensional scaling?

I'm trying to determine how many random initializations (restarts) I should use when performing an nMDS ordination. I understand I want to choose the solution that minimizes the stress, but how many ...
3
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1answer
97 views

Does Dimensionality curse effect some models more than others?

The places I have been reading about dimensionality curse explain it in conjunction to kNN primarily, and linear models in general. I regularly see top rankers in Kaggle using thousands of features on ...
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0answers
28 views

scale and rescale the data predicted from neuralnetwork

i am trying to use neuralnet or mlp function on my data and then iuse to predict on my test data output [B], at the beginning i am using this code to rescale my data : maxs <- apply(data, 2, max) ...
3
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3answers
174 views

Multiple regression - how to calculate the predicted value after feature normalization?

I'm currently doing the Andrew Ng machine learning course on coursera, and in Week2 he discusses feature scaling. I have seen the lecture and read many posts; I understand the reasoning behind ...
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0answers
41 views

Proper way to do multivariate analysis of relative abundance data table

I have a data table, which is a result of running software MetaPhlAn. The values are relative abundances of each microbe taxon in samples. Samples are independent of each other. I want to know how ...
5
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1answer
116 views

In multidimensional scaling, how can one determine dimensionality of a solution given a stress value?

In multidimensional scaling, how can one determine dimensionality of a solution given a stress value? From what I understand, stress value is inversely related to the number of dimensions of a MDS ...
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0answers
13 views

Manual multidimensional scaling in R

Given a matrix A, I want to complete Multidimensional Scaling by hand, instead of using any given R functions. As such, I have calculated the centered matrix ...
2
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2answers
46 views

Support Vector Regression and Data Rescaling

I am currently working on Support Vector Regression and I've read that it is recommended to implement data rescaling, e.g. to interval $[-1;1]$, to obtain better results. My first question is: should ...
3
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1answer
70 views

Interpret multidimensional scaling plot

I have data with 4 observations and 24 variables. To understand the underlying relationship I performed Multi-Dimensional Scaling (MDS), and got a plot like this: Now the issue is with the correct ...
1
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1answer
58 views

Unsupervised Learning on Multilevel/Multidimensional Data

I am working on a case-control study, where I for each individual have high dimensional data (like illustrated in the image). I would like to do both PCA analysis and Clustering on this data, but ...
0
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0answers
24 views

NMDS on data with different measure units

Is it correct to apply non-metric multidimensional scaling(NMDS) on data with different measure units and so, different ranges? Is there the possibility that the variable with higher values (due to ...
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0answers
28 views

What's right/wrong with using multi-dimensional scaling to analyze voting patterns of for members of an 11-member city council?

I'm trying to analyze voting patterns of an 11-member city council over a period of five years. Description of data file: each row (vector) is labeled as a councilmember; each column records ...
1
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1answer
177 views

The `Shepard` function in R package `MASS`

The function Shepard is listed in the help file for MASS::isoMDS, but nothing is said about it. What does this function do?
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0answers
250 views

Using similarity measures as distances

I have a matrix where each row corresponds to an observation with binary attributes, and I am interested in performing multidimensional scaling using cmdscale on ...
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0answers
107 views

Representing experimental data by unconstrained ordination: PCA, PCoA, or NMDS?

I have a dataset composed by presence of different bacterial families in function at different pesticide treatment. I need to find a good representation of my data but I don't know which method ...
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0answers
17 views

Random Forest Instance Proximities Training to Test

Using randomForest, I want to create a low-level projection of the instance proximities, as produced by MDSPlot(). However, I ...
0
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0answers
237 views

Fitting various environmental variables to an NMDS

I have two sets of environmental variables (e.g. river flow and river temperature statistics). I would like to assess which of these explains the highest amount of variance of a community community ...
3
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1answer
81 views

Using a distance matrix *with errors* to find the coordinates of points

(I asked this same question in stackoverflow, without getting any answer, but maybe this is a more appropriate forum.) I would like to find the coordinates of a set of points in 3D from a distance ...
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0answers
34 views

Clustering before or after ordination

Can someone explain the implications of performing clustering either before or after performing NMDS? I have some ecological data and I am performing a clustering analysis to identify communities of ...
1
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1answer
61 views

What does “ideal points” mean in multidimensional unfolding?

I am reading some materials about multidimensional unfolding and this concept "ideal points" are mentioned several times. I check these readings several times and could not find definition of this ...
0
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0answers
21 views

Isomap and Local MDS- embedding to linear spaces or not?

In Section 14.9 [1], it is said that Isomap and LocalMDS are embeddings into non-linear manifolds. The embedding is clearly a non linear operation, thus worthy of being NLDR. But As both Isomap and ...
4
votes
1answer
154 views

Similarities and dissimilarities in classical multidimensional scaling

I am having trouble reconciling between several terms in MDS. According to [1], Section 14.8, Classical MDS takes similarities as inputs. In [2], also cited in Wikipedia, Classical MDS takes ...
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1answer
937 views

interpreting NMDS ordinations that show both samples and species

I am using the vegan package in R to plot non-metric multidimensional scaling (NMDS) ordinations. I am using this package because of its compatibility with common ...
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0answers
101 views

Apply trained MDS model to new data

I have both a distance matrix and the original vectors, and am using MDS (Multidimensional Scaling) with R to generate vectors in more dimensions for the data. With dimensionality reduction (for ...
2
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0answers
584 views

non-metric multi-dimensional scaling in R: help with plotting and understanding Stress for isoMDS vs. metaMDS

I have a dataframe with 900 observations. There are 11 columns: 9 measured variables and 1 representing region. ...
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0answers
163 views

Using Non-Euclidean Distance Matrix in Multi-Dimensional Scaling

I'm trying to use a non-euclidean distance matrix in MDS. Specifically, I'm using cmdscale in R. The resulting eigen vector appears to have huge negative values. From what I've read it appears that ...
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0answers
146 views

Goodness of fit for multi dimensional scaling

What is considered to be an acceptable value for goodness-of-fit in multi-dimensional scaling (MDS). I tried to run an MDS analysis on my data with four dimensions in R. The goodness of fit comes ...
1
vote
1answer
134 views

Are there any versions of t-SNE for streaming data?

My understanding of t-SNE and the Barnes-Hut approximation is that all data points are required so that all force interactions can be calculated at the same time and each point can be adjusted in the ...
0
votes
1answer
19 views

Analysis of microbial community data collected from iPad swabs

We have taken a number of swabs (~200) from iPads used in our clinic, and after the first half we've introduced an intervention to disinfect all iPads after every use (in addition to the standard ...
2
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0answers
163 views

Non-metric Stress in 3 way Multidimensional Scaling (INDSCAL): individuals vs. group

My data consists of 19 participants giving dissimilarity ratings for every possible pairwise comparisons of a set of 12 epistemic adverbs (so each participant gives 66 ratings). The goal is to ...
0
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1answer
77 views

How do I apply MDS analysis on my data set?

Consider the following dataset (it is the emission probability matrix of a Hidden Markov Model): ...
2
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2answers
220 views

Understanding differences between large and small dimensional data when implementing algorithms

I'm working on a local outlier factor implementation based on the wikipedia entry : http://en.wikipedia.org/wiki/Local_outlier_factor This article seems to explain it in just two dimensional data. ...
3
votes
1answer
151 views

Reference for dimension reduction techniques

This is a follow-up question to Is PCA appropriate for comparing subsets of panel data?. It turns out that, yes, PCA is appropriate. But there are also many other ways to reduce n-dimensional data to ...
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0answers
303 views

nMDS in vegan for soil data

I am working with abiotic soil data such as bulk density, moisture levels and soil chemistry as response data (some quantitative some as percentages) and a mix of abiotic and biotic data as ...
2
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0answers
35 views

Using the results of a non-metric multidimensional scaling to derive a measure of deviance

I'm working with a dataset containing the taxonomic compositions of floras all over the world and performed a non-metric multidimensional scaling (NMDS) based on the relative proportions of plant ...
1
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1answer
230 views

Normalizing Vs. Scaling

Are the concepts of normalizing and scaling of data in conflict with each other? I am adding weights to my features, I have tried normalizing the weights and it didn't make any difference in the ...
0
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1answer
25 views

should weights be scaled too?

I am using supervised learning algorithms (specificly SVM) on my data. I know that scaling was needed for my input data. however as I am also adding weights (using pairwise comparison), I am not sure ...