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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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 ...
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1answer
27 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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37 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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40 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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13 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 ...
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53 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 ...
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1answer
40 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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21 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 ...
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15 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 ...
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10 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 ...
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1answer
80 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
251 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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39 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 ...
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191 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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92 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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81 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 ...
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62 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 ...
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13 views

Using MDS to find similarity of teams: How do I weight variables? Do I need to normalize variables?

I'm planning on using MDS to find the similarity between teams based on a number of variables. In one round of the analysis I want to weight some observations more than others, and I'm trying to talk ...
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31 views

In R, is there an exact method for the political compass test?

In R, I am looking, in an exact way, for the method (or packages) used on the political compass test (see politicalcompass.org in the link Take the test) In my case, I have one data set consisting in ...
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1answer
17 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 ...
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105 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 ...
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69 views

How proximity and multidimensional scaling relate? #random forest

I have downloaded randomForest package of Breiman and try to use function MDsplot to plot the proximity of the data like the example in the manual ...
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126 views

Can I run a multidimensional scaling analysis with purely categorical data?

I have data where participants categorized facial expressions using one of seven emotion labels (Angry, Disgusted, Fearful, Happy, Neutral, Sad, and Surprised). Can I take the resulting confusion ...
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1answer
52 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): ...
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2answers
144 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. ...
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1answer
127 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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200 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 ...
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33 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 ...
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1answer
160 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 ...
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1answer
19 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 ...
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1answer
266 views

How to : a brief intro to scaling and rescaling data ( inputs) for supervised learning algorithms

I understand the concept of scaling and that it improves results in SVM's and NN's. however I would like to find somewhere where is is explained, in easy "layman's terms" terms. of how it is done. I ...
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1answer
56 views

Scaling in SVM (why and how to , plus references)

Hi I know why feature scaling is preferred in SVM, I have two questions: 1-does anyone know of legit articles of books explaining it. I am writing my thesis and I need references. It doesnt have to be ...
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1answer
95 views

Is it posible to perform the inverse of multidimensional scaling analysis

We have lot of 3D data and we reduced it to 2D for performing fuzzy clustering and obtaining prototypes. We used some matlab functions that were very well documented. Now we would like to see to which ...
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1answer
379 views

How to distance and to MDS-plot objects according their complex shape

Suppose I have four basal forms of signal (blue, purple, red, green). I also have created transition forms between each other. If you carefully look on the picture below, you can see that for example ...
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35 views

Suitable plot for 5 dimensional feature vectors?

I have a list of personality scores obtained from 100 people, based on the Big-Five personality test. Each person has one score for each of the five assessed traits. I put these scores into a 5 ...
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97 views

Is it correct to combine PCA and NMDS axes in a multiple regression?

I am considering to do a multiple regression in which some of the predictive variables are PCA (principal components) axes whereas others are NMDS (nonmetric muliple dimension scaling) axes. I would ...
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1answer
186 views

Multidimensional Scaling “eurodist”

I have a question regarding Multidimensional Scaling. I used the dataset eurodist from the package datasets to generate a 2 ...
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4answers
724 views

Performing PCA with only a distance matrix

I want to cluster a massive dataset for which I have only the pairwise distances. I implemented a k-medoids algorithm, but it's taking too long to run so I would like to start by reducing the ...
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1answer
709 views

Multidimensional scaling using Python

I have 6,000 points for which I have all pairwise distances in a distance matrix. I want to get an idea whether these data were generated by a mixture of Gaussian distributions so I'm trying to get a ...
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38 views

Multidimensional scaling of variables with multiple sub-features?

Let's say I have a year's worth of magazine issues (January, February, March, etc), and I want to visualize the differences among them. The classic example of multidimensional scaling (MDS) would have ...
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59 views

Steps to follow for correspondence analysis when each brand is not shown to every respondent

I want to understand the steps followed for correspondence analysis when each brand is not shown to every respondent. Till now I used to assign a number (proportion) to each brand for each attribute ...
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1answer
697 views

Guttman's smallest space analysis

I am trying to replicate an analysis as conducted by Raven, et al. (1998). A scale is analyzed with Guttman's smallest space analysis. First of all I wasn't able to find a suitable procedure in R and ...
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1answer
207 views

Kernel PCA with an SVD algo

Suppose that I have a great algo for calculating the SVD and I want to do Kernel PCA. It is possible to first apply the Kernel function to my data and then run the SVD algo on the transformed data?
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1answer
90 views

Multidimensional quantiles

I have 1000 observations with 2 continuous variables : Observation ID | X | Y
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410 views

Gower distance and MDS: How to determine which variables count?

I have morphological data from two different determined groups (It and Nd), where the variables are heterogeneous (continuous, ...
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0answers
127 views

When using Nonmetric Multidimensional Scaling, is there an explanatory metric similar to loadings in PCA?

As a beginner to MDS, here is my thought process: Given a data set of environmental factors that may effect a certain sites, when I run a PCA on each site I get a list of principal components. If I ...
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692 views

Multidimensional scaling in R with Spearman's rank correlation

I'm new to MDS, but I found some good starter code here (http://mhermans.net/static/postdata/r-examples/neighbours-mds/neighbours-mds-example.html) and which I borrowed for below. The code works fine, ...
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2answers
2k views

Perform feature normalization before or within model validation?

A common good practice in Machine Learning is to do feature normalization or data standardization of the predictor variables, that's it, center the data substracting the mean and normalize it dividing ...
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1answer
134 views

Multidimensional scaling for big dissimilarity matrix

I have a large symmetrical dissimilarity matrix of dimension 300 000. Can you please suggest the multidimensional scaling algorithms that can work with such large data? Input of course can be the ...
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0answers
79 views

Transforming distance matrix to univariate data

Is there a valid mathematical or statistical approach to converting site by site distance objects/matrices to a univariate site vector or column in a dataframe? I have a Euclidean distance matrix for ...