# Questions tagged [dimensionality-reduction]

Refers to techniques for reducing a large number of variables or dimensions spanned by data to a smaller number of dimensions while preserving as much information about the data as possible. Prominent methods include PCA, MDS, Isomap, etc. The two main subclasses of techniques: feature extraction and feature selection.

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### How can an autoencoder unroll the swiss roll?

I'm trying to unroll the swiss roll (from 3D to 2D) using an autoencoder, but it keeps getting stuck in local optima: the swiss roll ends up squashed rather than unrolled. It's no better than using ...
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### What's wrong with my solution to canonical correlation analysis (CCA) using the SVD

I am working through the derivations for solving CCA in A Tutorial on Canonical Correlation Methods. Right now, I am trying to solve CCA using SVD (bottom of page 95:7). For completeness, I include ...
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### Recovering a distance matrix from nonnegative sparse correlation matrix?

After doing extensive literature research in all sorts of science I am completely puzzled. I am trying to find out what the state-of-the-art techniques would be to recover a (let's say euclidean) ...
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### Relationship between Intrinsic Dimension and Principal Components - PCA

I have a data space with 144 features 12x12 grid : and I know that I can fully describe it in M = 6 features with the help of PCA. My data space has intrinsic dimensionality = 3 (three grades of ...
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### Removing the influence of undesirable features in clustering

In some machine learning problem, we have objects represented as feature vectors $X$. We want to cluster them. We want some features of $X$ to have no influence on the clustering. Call $X_1$ such a ...
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### Dimmensionality reduction for highly dimensional multivariate time series with few time steps

I am looking for a dimmensionality reduction technique that is ideally compatible with LSTM. The dataset I am working with is a multivariate time series with 10 time points and ~13000 features. The ...
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### Dimension in Locally Linear Embedding (LLE)

I am using LLE to do nonlinear dimensionality reduction. In my understanding, in the step 3, the eigendecomposition problem is with respect to the matrix M which has the dimension NxN (N is the number ...
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### Dimensionality reduction with discrete comparisons

I have data from many people with the following shape (from a survey): $a$ is more similar to $b$ than $c$ where $a, b, c$ are visual stimuli. I would like to visualize the data in a nice one-...