Questions tagged [multidimensional-scaling]

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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Multidimensional scaling with censored and missing distances

I would like to apply MDS to a high-dimensional distance matrix but the difficulty is than the distance matrix contains many missing and censored values (i.e. distances like >8). Does anyone know of ...
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7 views

Parallel analysis for principle components analysis and Multi-dimensional scaling

Does the same method for conducting a parallel analysis for principal component analyses apply to find the cut-off point for multidimensional scaling? Under the pretense that PCA and linear MDS are ...
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15 views

Hierarchical clustering on principle components for multidimensional scaling

Essentially I have a data set of distant objects in which I've loaded onto factors using a multidimensional scaling technique. From my understanding, the factor loadings only differ between MDS and ...
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5 views

Setting the number of dimensions in an MDS

I have two distance matrixes. The distance matrixes are two topics of a Latent Dirichlet Allocation. The distance is computed based on the probability distribution of the 6 words with the highest ...
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24 views

k-means/k-nearest neighbours on multi-dimensional scaled data

I used the Python manifold library for multi-dimensional scaling on my distance matrix. Can I use k-means or k-nearest neighbours on ...
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56 views

How to visualize proximity score in Random Forests

For a Random Forest, we can construct a N x N (where N is the number of data points) proximity matrix ...
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24 views

Does a PCoA (or MDS) assume normality of the variables behind distances?

More precisely, if I conduct a cmdscale (classical multidimensional scaling) on an Euclidean distance matrix by considering $n$ observations of $p$ variables i.e. $D_{ij}=\sqrt{ \sum_p (x_{ip} - x_{...
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34 views

Visualizing a Latent Dirichlet Allocation (LDA) by Multidimensional Scaling (MDS)

I did an LDA with four topics for four different Smartphones. This was done using customer Reviews of Amazon. ...
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16 views

Procrustes Analysis: Interpreting an Impulse Diagram of Residuals

I have applied 3 MDS techniques to a data set. They are; classical metric scaling, Kruskal's non-metric scaling and Sammon's metric least squares scaling. In order to compare the 3 configurations I am ...
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116 views

K means clustering of MDS data

I've recently run a very large data set through a multidimensional scaling analysis and am attempting to cluster the results into groups. I've read a few papers that utilize hierarchical clustering to ...
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22 views

Principle coordinates analysis - visualization and assigning text to graph in r [closed]

I've recently run a principle coordinates analysis in r with ~ 200 subjects and 17 binary variables. I've plotted my points successfully, the challenge I'm facing is that when I plot the text with the ...
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23 views

When is the result of multidimensional-scaling unique up to isometry?

What conditions on the ambient space and/or the given matrix of dissimilarities guarantee that all point configurations that minimise the error function of multidimensional scaling (MDS) are congruent,...
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55 views

How to localize points from an incomplete distance matrix in R?

Suppose you have 3 shops and 2 supply units, and you only know the 6 pairwise (Euclidean, assuming 2D) distances between each shop and each supply unit, but not the pairwise distances between the ...
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35 views

Interpretation of a Multidimensional Scaling graph

I used the variables "number of children in the family", "Wife's age", "Religious wife", and "Midia exposure". The "number of children" and "Wife' age" are both numerical variables. "Religious wife" ...
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161 views

Project new point into MDS space

I am trying to project a new point A(x, y, z) into a predefined MDS space in R. This is what I have so far: ...
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1answer
139 views

Is it necessary to deal with the outliers if we perform Normalisation on the data?

I am wondering, if it is necessary to remove outliers from the dataset if we perform Normalisation on the data as after Normalisation, all the values will shrink to value between 0 and 1. So, is it ...
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71 views

Data Scaling: is multi-dimension scaling equivalent to uni-dimension scaling for same range features?

I am given an $n \times m$ matrix $\mathbf R$ where $n$ is the number of $users$ and $m$ is the number of $items$ - this matrix is usually known as the rating matrix within recommender systems domain. ...
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35 views

Quantify area of community variance in a NMDS?

I have an NMDS analysis in which I used to visualize distances between macroinvertebrate community compositions within four treatments referring to acid mine drainage condition. The plot looks like ...
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34 views

How to combine count data and percentage data for ordination methods?

The system I have has multiple sites each of which was surveyed for biodiversity. Some species are too hard to count individually so we used percentage cover, others were easy to count so we have ...
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231 views

Negative eigenvalues when computing MultiDimensional Scaling given nonnegative distance matrix

I am using the Smile MDS https://github.com/haifengl/smile/blob/master/core/src/main/java/smile/mds/MDS.java and occasionally running into: ...
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19 views

MDS with one column derived from others

I'm applying a MDS to a table composed of many countries (rows) and some socioeconomic data (columns). The columns are: GDP, Imports, Exports, Population Density, Population Growth, Urban Percentage ...
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270 views

How to embed in Euclidean space

I have what I think might be a standard machine learning problem but I can't find a clear solution. I have lots vectors of different dimensions. For each pair of vectors I can compute their ...
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1answer
79 views

Book about ordination in ecology

I am looking for a book that would cover a lot of different ordinations techniques (indirect gradient analysis e.g. PCA, CA, DCA, MDS, nMDS but also direct gradient analysis e.g. CCA, CCorA, RDA) with ...
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43 views

Method named 'BEST' in multivariate analysis - what is it?

I've read a few ecological papers where a 'BEST' procedure is used to assess effect of environmental parameters on biological community composition, after multivariate analysis like MDS. For instance ...
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501 views

Feature scaling/normalization and prediction

I have a dataset which I have split into a training and a test set. I have thereafter applied normalization on the training set and saved the mean (U) and standard deviation (SD) estimated based on ...
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37 views

Multidimensional scaling with periodic boundaries

For a specific application at hand, I need to visualise samples from a high dimensional space into 2D, while respecting their distances as much as possible. Normally, I would simply use ...
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1answer
157 views

cmdscale in R returns difficult to explain results

PROBLEM DESCRIPTION: We have two very similar 8D datasets. The OLD has 107 records, the NEW has 111 record (107 from the OLD plus 4 additional record). The NEW dataset download The OLD dataset ...
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335 views

Scaling unknown time series for prediction with RNN

I'm trying to build a RNN model to predict arterial blood pressure (ABP) time series based on two other time series, namely, ECG and PPG. It is available to me a set of multivariate time series of ...
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511 views

Should I standardize my data or not?

I am currently working on a dataset concerning the color magnitude of astronomical point sources. There are 9 covariates, each representing a specific color of a point source. I used k-means, ...
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112 views

Which dimensionality reduction technique preserves the k nearest neighbors (euclidean space)?

I'm looking for a lower dimensional projection of data such that the k nearest neighbors (in Euclidean space) in high dimensions remain the k nearest neighbors in low dimensions. I found that Isomap ...
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1answer
126 views

Are NMDS Axis the same calculated for one or more dimensions?

I used NMDS axes of an ecological community as proxys for the community similarity in the different samples. I would like to "quantify" the importance of the different NMDS axis according to the ...
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528 views

Convert distance matrix to coordinate(s)

I have a (SNP) distance matrix that I would like to convert to coordinates (ideally just one linear scale) in R. I am planning to use the transformed coordinates in principle component analysis along ...
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1answer
261 views

Why normalize data after doing Multidimensional scaling?

I am running simulations from a paper on graphical clustering based on latent positions. Essentially, the first step is to do Multidimensional Scaling on the Adjacency matrix, after which the authors ...
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286 views

MDS with city distances, some values missing

I have a matrix of distances between cities and I want to use [Multidimensional scaling] (MDS) to calculate the locations of the cities. What MDS algorithm is useful for this? Is it available in ...
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54 views

Feature scaling and when to use which

I am looking into running regression on a multivariate data set. I am looking into different ways to scale my data: standardization, L2 and L1 normalizations. In what case would you use which method? ...
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299 views

When the distance metric is not Euclidean, the metric Multidimensional Scaling (MDS) is nonlinear?

As it is commonly known, classic metric MDS (under Euclidean distance metric) is a linear dimension reduction method (equivalent to PCA), and it is also known to us that non-metric MDS is nonlinear, ...
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1answer
390 views

Can MDS coordinates be used as variables in further analyses?

I have calculated an NMDS from vegetation community data taken from two habitat types using the metaMDS() function in vegan. My ...
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117 views

How to normalize three variables into one ordinal scale?

I have three same scaled variables (Strategy 1: Stay, Strategy 2: Move, Strategy 3: Move far) which I want to compare in a heatmap for different locations. For each strategy an operator earns a ...
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1answer
2k views

Scaling on Categorical Variables for KNN Imputation

Problem: I am attempting to impute on a data set in R (6000+ rows, 55 columns) with high NA proportions in most variables (from 10 - 80% missing) and have found evidence to support the KNN approach to ...
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1answer
54 views

How do I scale/standardize one set of data that is non-linear (due to temp variations in a device) to that of a data set that is linear?

I have data from two instruments over time. One of the instruments does not show a linear trend due to an anomaly but I want to standardize that data against that of the second instrument because I ...
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676 views

Pros and Cons of MinMax Normalization vs. Standardization

I have a large dataset with 800 columns and 6,000,000 rows with many dummy variables (70%+). I want to Normalize it. Given that so many variables are binary, taking values 0 or 1, I am tending ...
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205 views

NMDS anomaly - data does not support point placement

My data: Tracking forest communities (via species abundances) in various forest plots across time. My approach: Non-metric Multidimensional Scaling ordination I performed NMDS (using ...
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750 views

Contribution of variables on axis in PCoA

I am trying to analyze data using Principal Coordinates Analysis (Classical Multidimensional Scaling (CMDS)) in R. I've tried some different ways (i.e., pcoa {ape}, ...
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1answer
381 views

Multidimensional graph representation

I would like to know how to plot the following 30 observations on a graph, with 1 color for each country, using Kmeans or other methods on R. Each of the 40 independent variables can take value from 0 ...
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85 views

Is the Normalized Information Distance (NID) a euclidean distance?

I'm trying to determine if I can plot my dissimilarity matrix (which is a Normalized Information Distance / the "Universal Distance Metric" dissimilarity matrix), in a lower-dimensional space using ...
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1answer
346 views

Alternative to MDS plot for random forest visualisation

I'm using R and 'randomForest' package for binary classification. I can MDS plot (from the same package) the initial (more or less) class separation based on training set. However, this doesn't allow ...
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1answer
30 views

Quantifying relationship between items, given many groups of items

I'm having trouble researching this, or even writing this question, because I'm not sure what this is called and I'm not familiar with a lot of the terminology. The best I can do is an example. Let's ...
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969 views

Convert Pearson correlation matrix into dissimilarity matrix

I would like to execute multidimensional scaling (MDS) based on a matrix of Pearson correlation coefficients. The sklearn.manifold.MDS function takes a dissimilarity matrix as an input and I therefore ...
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33 views

Analyzing jagged multidimensional data

I've been looking into this for a while now, but I don't think I know enough of the terminology to phrase this well enough for Google (my apologies). So essentially I'm looking at motion capture data ...
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303 views

non-metric multidimensional scaling in R - smacof: why minimum stress model is so different from torgerson model?

Trying to understand how nMDS is applied with smacof package in R. From smacof vignette I understand that one can attempt to ...