# Tagged Questions

Dimensionality reduction refers to techniques for reducing many variables into a smaller number while keeping as much information as possible. One prominent method is [tag pca]

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### Distance metric for all categorical data

I have categorical data in the form of Y/N of 23 attributes and containing 200+ records. I need to compute the distance between the attributes with their class label. Which metric can be used?
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### Unsupervised Dimensional reduction for mixed data types

I have a data set with about 50K rows and 100 columns. You can consider every row to be representing one restaurant. My goal is to calculate dissimilarities between all the restaurants - Gower's ...
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### More complicated classifier vs data preprocessing

For example if we have two options to use non-linear classifier like SVM with kernel or use linear classifier like linear SVM with data preprocessing like some non-linear dimensionality reduction ...
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### Deep learning with $p \gg N$ and on multi-modal and heterogeneous data

I work in a problem where: The input data is a collection of time series from different sensor modalities, where each sensor comes with different acquisition rates and dynamic ranges The number of ...
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### Variable reduction by means of ANOVA?

I have a typical problem with several variables and a large amount of data which are not important right now. The goal of the study is to relate variable $Y$ with variables $X_1,X_2,...,X_n$. I have ...
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### Reducing the dimension of an embedding

Let $O \in \mathbb R^{p\times m}$ be a data matrix of observations. Suppose we are given a model $\mu : \mathbb R^n \rightarrow \mathbb R^m$ which is able to approximately fit the observations. Fix ...
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### Interpreting “elliptic” shape in visualization of high-dimensional dataset, e.g. COIL-20?

There exist an image dataset referred to as COIL-20. There are several paper that describes different dimensionality reduction methods and apply them to produce a 2d plot of this dataset. These ...
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### Supervised dimensionality reduction-applications

What are the applications or advantages of dimension reduction regression (DRR) or supervised dimensionality reduction (SDR) techniques over traditional regression techniques (without any ...
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### Dimension Reduction to a Relative Single Value After PCA in R?

After reducing the dimensions of a matrix using PCA, I want to transform the "big" components (i.e. they cumulatively account for 90% of the variance) of each row into a single value. The value does ...
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### seeking advice on dimension reduction for spacial and time-series data

I have 200 data sets, each of them has roughly 600 rows with some exceptions (some have about 2000). Each data set represents data collected from a test subject, and the data in each one of the 200 ...
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### High Dimensional Data, Multicollinearity, and Skewed Dependent Variable - a good approach?

I have a regression problem that has three key issues I am trying to tackle. The first is the data is a p >> n problem, e.g. I have about 1500 features but only about 150 examples. The second is that ...
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### Variation explained by single variable

I’m trying to find a way to measure how much a single variable ‘summarizes’ a full set of continuous variables. For instance, in a PCA the first principal component will explain a certain percentage ...
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### Reduce matrix size for hierachical clustering

I have 500.000 measurements (rows) for 20 samples (columns) and I want to do hierarchical clustering to see of I can detect samples groups in the data. Clustering on the columns works fine in R: ...
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### Finding the projection used in multidimensional scaling

Background I have a set of data points in high-dimensional (512D) space that I wish to map to 2D for visualisation. I am interested in observing in 2D the (approximate) relative distances between the ...
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### Orthogonalization for dimensionality reduction

There are some methods like singular value decomposition (SVD), principal component analysis (PCA), factorial analysis and many more that are used to reduce a high-dimensional dataset into fewer ...
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### Dimension reduction: preserving original characteristics of data or not?

I am new in dimension reduction techniques, I need to know what are the advantages and disadvantages of Preserving the original pattern of data after making data reduction Making reduction and ...