# Questions tagged [dimensionality-reduction]

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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### Illustrating the dimensionality reduction done by a classification or regression model

Tl;DR: You can predict something, but how do you explain the prediction? EDIT: I have built a website that tries to answer this question with means of embedding / visually clustering data according ...
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### Dealing with high dimensionality datasets

I have data of dimensionality (25000, 100, 500) i.e. 25000 rows each consisting of a 2 dimensional 100 X 500 matrix. Currently I am only applying CNN for ...
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### Is t-SNE good at clustering instances with the same trend?

I have a dataset of time-series data with 50k examples and a length of 90, like the images showed below: I was wondering whether t-SNE or any type of dimensionality reduction could group the ...
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### Dimenson reduction from a cosine similarity matrix

I have a silly little question: I have 200 press articles (string), I vectorize these articles with an embedding model (sentence embedding), so I have 1024 values per article. I then have a 200 x 1024 ...
1 vote
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### Dimensionality reduction to correlate large number of variables

So I have this dataset with about 750 variables (columns) and 50,000 rows of entries. I would like to reduce the dimensionality of the dataset to say 25-50-100 dimensions and then compute a ...
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### Extension of NMF to 3D

AFAIK, Non-Negative Matrix Factorization (NMF) is the procedure of looking for matrices $A$ and $B$ such that $$Data_{ik} = \sum_j A_{ij} B_{jk}$$ My data matrix is in fact 3D. I would like to fit ...
1 vote
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### Which algorithm can be used to reduce dimension of multiple time series?

In my dataset, a data point is essentially a Time series of 6 feature over a year per month so in all, it results in 6*12=72 features. I need to find class outliers so I perform dimensionality ...
1 vote
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### How to structure my data into features and targets for PCA on Big Data?

I want to apply the PCA algorithm from Scikit-Learn.(https://scikit-learn.org/stable/modules/generated/sklearn.decomposition.PCA.html ) At the part where I have to separate the features and the ...
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### How can I reduce the number of dimensions using a Clustering algorithm in a mixed dataset?

I am working with a mixed data set, corresponding to TV consumption data, with the aim of reducing the number of features to only those relevant to detect TV consumption patterns (or consumption ...
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### What can we learn from PCA on non linear data?

Suppose we have dataset with 10 features which are not linear: ...
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### Multidimensional scaling producing different results for different seeds

I took the data from here and wanted to play around with multidimensional scaling with this data. The data looks like this: In particular, I want to plot the cities in a 2D space, and see how much it ...
1 vote
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### Decision trees and Curse of Dimensionality

Since decision tree algorithm splits the training dataset one feature at a time, how the heck is possibly that it suffers from curse of dimensionality ?
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### TSNE plots of random data subsets are vastly different but labels are still clearly separated - what conclusions can we draw about the dataset?

I scraped a dataset of match data in a video game and labeled them according to their outcome (0 for loss, 1 for win). I wanted to see if there was actually any inherent relationship between the ...
1 vote
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### Beginner basic clustering model and one-hot encoding?

I have a dataframe of natural disaster incidents in Afghanistan from 2016 - 2023. Column names: REGION (Northern, Eastern etc) PROV_CODE (province) PROV_NAME DIST_CODE (district) DIST_NAME INC_DATE (...
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### Beginner clustering project, what are the input features and how do I analyze the data?

I am a beginner to data science. I have this dataset on natural disaster events in Afghanistan from 2016 - 2017. Columns: REGION (ex. North, North West, etc) PROVINCE_NAME (kind of like US 50 states) ...
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### How to deal with a small extra cluster in a tabular data?

I am working on a high dimensional tabular dataset with 1600 features and 9440 rows. No matter how I select the features, when I try to project my data into a 2d or 3d graph using dimensionality ...
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### Is there any advantage to providing multi-dimensional input to torch modules?

Most layer types in torch.nn such as torch.nn.Linear accept input with more than one dimension. Is there any advantage in doing so if you can shape your data to represent a certain arrangement in ...
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### How to explain the new features after a PCA?

Let's say I made a PCA in which I reduced from 10 dimensions to 3. And it clusters the classes correctly, but how do I explain which dimensions are better to predict? It is obvious that the 3 ...
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### Can I do incremental learning with the sklearn implementation of Linear Discriminant Analysis

I have a large number of pictures that I would like to use LDA on. However, it requires too much memory, so I was wondering if it would be possible to make the learning incremental, using a sklearn ...
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### What is the best sampling strategy for correlation analysis?

I have a big dataset, and i want to finds subspaces with high correlation among features. I want to take only samples of data. So, what is the best sampling strategy in this context. Thanks
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### A query with regard to removing data from a dataset before clustering. - conceptual

I am in posession of data with regard to my domain which is energy economics. The dataset contains daily data on daily electricity demand along with the daily capacities of wind and solar plants for ...
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### What are the differences among Proper Orthogonal Decomposition (POD), Singular value decomposition (SVD) and principal component analysis (PCA)?

Proper Orthogonal Decomposition (POD), singular value decomposition (SVD), and principal component analysis (PCA) are three eigenvalue methods used to reduce a high-dimensional data set into fewer ...
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### Guidance needed with dimension reduction for clustering - some numerical, lots of categorical data

I've my data in a Pandas df with 25.000 rows and 1.500 columns without any NaNs. Of the columns about 30 contain numerical data which I standardized with StandardScaler(). The rest are cols with ...
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### Linear Model With Highly Correlated Attributes Producing Inconsistent Weights

I know that having correlated attributes violates the linear model assumption of independent attributes, and I'm not interested in creating a more sophisticated model to tease apart the dependent ...
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### An autoencoder setup for anomaly detection

I am doing anomaly detection using machine learning. i have tried different models like isolation forest, SVM and KNN. The maximum accuracy that I can get from each of them is $80\%$ accordind to my ...
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### What does the long curve-shape t-SNE mean?

I use 1-D CNN input 1*512 size time series data which randomly fragment segment, the output will classify input into 10 classes. After training the CNN, I apply t-SNE to the prediction which I fed in ...
1 vote
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### Density distribution for feature analysis

I trained a ML model on original data with 6373 features, then I trained the same model on compressed data (using autoencoder) and I got an improvement. Finally, I trained the same model on reduced ...
1 vote
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### How to properly visualize high-dimensional embeddings along with the decision boundary in 2-D?

I have a number of embeddings (300-dimensional FastText vectors for each instance of each class) that I apply a classifier to (Logistic Regression for now). I want to visualize the embeddings as well ...
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### How to improve the preservation of the global data structure in UMAP?

I have a dataset, where the features are comprised of points arranged in a regular grid on a simplex. Each of these points are defined as follows: A point $\mathbf{x}$ on the simplex can be ...
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### Reducing the dimensionality of word embeddings

I trained word embeddings with 300 dimensions. Now, I would like to have word embeddings with 50 dimensions: is it better to retrain the word embeddings with 50 dimensions, or can I use some ...
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### Can TSNE and other visualisation methods separate multivariate normal blobs?

Consider we have two classes of points. Both of them come from a multivariate normal distribution with an unrestricted covariance matrix. Let's assume, that the densities of those distributions do not ...