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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10 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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23 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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22 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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21 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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6 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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26 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
12 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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0answers
64 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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31 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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52 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
40 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
82 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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13 views

Add species characteristics, alongside count data, when using ordination

I have count data by site for 15 species, however I also have average size by species by site. I'm looking to use ordination techniques (non-metric multidimensional scaling) to reduce dimensionality ...
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73 views

Multidimensional Scaling: Interpreting output of different distance matrices (Euclidean or correlation)

I would like to understand the difference between using a Euclidean distance matrix or correlation matrix as input to a nMDS algorithm. I have completed MDS plots of both, and while similar, the ...
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1answer
101 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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120 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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29 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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25 views

Multidimensional Scaling - external scale regression using standardized or unstandardized weights

I am having trouble determining whether to use standardized or raw regression weights when using multiple regression to interpret Multidimensional Scaling plots. Different textbooks seem to advocate ...
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1answer
100 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
18 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
133 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
44 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
65 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
243 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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26 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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47 views

Converting data from classical scaling in MDS

I implemented the classical scaling and obtained the relative coordinates in Java. I want to translate these relative positions, but I am not getting how I should be doing this. This is just like: ...
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72 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
141 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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139 views

Draw biplot on nonclassical MDS interpration?

How can I draw similar biplot like you can on PCA, so I could compare these two visualization techniques / methods? I got dissimilarities and distances of my data in simple plot, but I would like it ...
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3answers
301 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
508 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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32 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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51 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
488 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
155 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
74 views

Multidimensional quantiles

I have 1000 observations with 2 continuous variables : Observation ID | X | Y
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258 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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104 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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469 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
1k 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
98 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
63 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 ...
2
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0answers
208 views

Why does some model-based clustering fail to fit with a large number of dimensions?

I am attempting to cluster data using Mclust. The data is originally from a dissimilarity matrix, transformed via multidimensional scaling in R (MASS::isoMDS). As I ...
4
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1answer
253 views

whether to rescale indicator / binary / dummy predictors for LASSO

For the LASSO (and other model selecting procedures) it is crucial to rescale the predictors. The general recommendation I follow is simply to use a 0 mean, 1 standard deviation normalization for ...
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1answer
272 views

Analysing data measured as proportional composition

I have a data set on the proportional composition of marine substrate for different locations which I would like to compare. For example, one replicate transect within a location may be 50% sand, 25% ...
3
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1answer
617 views

How to interpret variation explained by principal coordinates?

I have recently seen a couple of Principal Coordinates Analysis (PCoA) projection plots which show "percentage variation explained" by the respective principal coordinates. Given that the analysis is ...
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24 views

Issue of multiple scaling method

I plan to use multiple scaling in R and start with a toy example. There are two matrices. The first contains 10 observations and the second one contains two replicated rows. It appears that the ...
2
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0answers
24 views

How to enhance the performance of naive CMDS?

I'm performing Classical MDS on the distance over columns of binary matrix. The result is like this: The points lies on two lines vertical to each other. I don't think useful information is shown ...
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3answers
411 views

How to project high dimensional space into a two-dimensional plane?

I have a set of data points in a N-dimensional space. In addition, I also have a centroid in this same N-dimensional space. Are there any approaches that can allow me to project these data points into ...
3
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1answer
357 views

MDS and PCA eigenvalues and eigenvectors

I understand that Multidimensional scaling (MDS) is same as doing Principal Components analysis (PCA) if Euclidean distance is used, this is known as Metric MDS. But I came across this in a book that ...