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Questions tagged [dimensionality-reduction]

In machine learning and statistics, dimensionality reduction or dimension reduction is the process of reducing the number of random variables under consideration, and can be divided into feature selection and feature extraction.

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If the number of features are much larger than the number of examples in dataset does svm still work?

Linear regression doesn't work if the number of features is larger than the dataset size; unless regularization is applied. Does svm also have similar issues? Will it not give a good approximation if the number of examples are less in comparison to…
user6564906
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On hash functions application in cryptography vs feature

I have only started learning feature hashing so I need help in understanding if I can apply the hash function expressed mathematically as https://en.wikipedia.org/wiki/Tent_map. and one such application of Tent map is in cryptography -- Paper 1:…
Sm1
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Scikit-learn PCA .fit_transform shape is inconsistent (n_samples << m_attributes)

I am getting different shapes for my PCA using sklearn. Why isn't my transformation resulting in an array of the same dimensions like the docs say? fit_transform(X, y=None) Fit the model with X and apply the dimensionality reduction on…
O.rka
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How to obtain the eigenvalues after performing Multidimensional scaling?

I am interested in taking a look at the Eigenvalues after performing Multidimensional scaling. What function can do that ? I looked at the documentation, but it does not mention Eigenvalues at all. Here is a code sample: mds =…
Uther Pendragon
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Dimension reduction on categorical variables based on values of continuous variable

I am interested in predicting a continuous variable reflecting vegetative production using a collection of land use categorical variables. The dataset is a pixel-level dataset, where each pixel has a value of vegetative production and a land use…
Emily
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Can I use t-SNE when the dimension is larger than the number of data?

I am using t-SNE with the matlab code from this web site (https://lvdmaaten.github.io/tsne/). However, there is an error whenever I run this program with the data's dimension is larger than the number of data. The code below is the code I use…
Minkyu Choi
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Dimensionality reduction for high dimensional sparse data before clustering or spherical k-means?

I am trying to build my first recommender system where i create a user feature space and then cluster them into different groups. Then for the recommendation to work for a particular user , first i find out the cluster to which the user belongs and…
rehan ali
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Are these the irreconcilable cons of using DictVectorizer in Scikit learn?

I have 5+ million data to predict people's race. One textual feature gives rise to tens of thousands more. For example, name 'Smith' give rise to 'sm', 'mi', 'it'... etc. I then need to transform it into some sparse matrix from…
KubiK888
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how to implement the example of letter 'A' in the wiki of nonlinear dimensionality reduction

When I read the wiki of nonlinear dimensionality reduction(https://en.wikipedia.org/wiki/Nonlinear_dimensionality_reduction), I found the interesting example of letter 'A', so I want to implement it using python, but I get some problems here. The…
user85768
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Dimension reduction of a 2D-Image via PCA

I want to reduce the dimensions of 2D-Images. I have image patches of size 100x50 and I want to reduce the dimension of these patches. Do I need to first convert the patch(100x50) into a vector(5000x1) and then apply PCA to reduce the dimension or…
Frq Khan
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Searching a deep autoencoder example for dimensionality reduction

I want to configure a deep autoencoder in order to reduce the dimensionality of my input data as described in this paper. The layer sizes should be 2000-500-250-125-2-125-250-500-2000 and I want to be able to pull out the activation of the layer in…
QuickBrownFox
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Application of clustering in segmentation and reduction

I have a multi dimensional time series data that are the features for action recognition. I have 20 variables representing 3D joint positions, quaternion values, mean, euclidean distance between joints. I have applied kmeans clustering with k=4.…
Srishti M
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Matlab:Issues in clustering

I need to perform dimensionality reduction on a multi dimensional data set data set that has been clustered using k-means. The data contains positive and negative real numbers obtained from sensor readings of sensors placed on a haptic glove. The…
Srishti M
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How to use Linear Discriminant Analysis for projection in MatLab?

Is it possible to project a multidimensional data to a 2D map using LDA? It seems that the tool Matlab provided does not provide such functions... Thanks for reply. My data now is having 6 classes, so does it mean that if I have 6 classes, I can…
faz
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Is This Graph Embedding Possible & Does It Have A Name?

I want to project an undirected graph into the 2d plane such that: the euclidean distance preserves the stepwise distance (i.e. if the shortest path between A and B is shorter than the shortest path between C and D, then the euclidean distance…
Adam Golding
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