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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Understanding clusters after applying PCA then K-means

I have a dataset grouped by customer level, and the rows are sum_mexico, sum_uk, ... etc to indicate if the customer has spent money at stores in those countries..similarily counts for these as well. ...
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How can I reduce the volume of data (No column to be dropped)?

I have a data set of 80,000 samples (40k 3 axis accelerometer and 40k Gyro data). I am trying to implement KNN and Random Forest for activity recognition on ESP8266 Node MCU. The limited memory of the ...
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Can I cluster an aggregated data-set (grouped by) and apply dimensionality reduction?

I have data of sales, however it is in the millions, about 500M rows. I aggregate this data by factors such as location, shoptype, country_of_shop, cardtype, and then the aggregated statistic is: ...
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Compare distance between embeddings in different dimensions

I am working on a problem with CNNs. After the convolutional layers, comes a "flatten". One could interpret that as a representation of the input image in some high-dimensional continuous ...
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MCA and FAMD bad scores for UCI census dataset

Attempting dimensionality reduction on the Census-Income (KDD) Data Set. The dataset is a mixed dataset with continuous and categorical features. PCA works fine for continuous variables, reduced down ...
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Dimensionality reduction for geometric curves using an autoencoder - what is wrong?

I am trying to play with toy models in order to study autoencoders. In particular, I want to do dimensionality reduction for simple geometric curves in 3D. First, I take a toroidal helix. ...
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Difficulty understanding the dimension differences in kernel PCA

In Kernel PCA, the kernel trick works because we can show that there is an equivalency between eigenvectors of the kernel matrix and eigenvectors of the covariance matrix. I know the math to go from ...
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Clustering pre-defined groups of data points under dimensionality reduction

I have a dateset of around a million observations, and each observation (300 features) belongs to one of around 300 groups. The set of observations of one group does not directly correspond to the ...
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Using the curse of dimensionality for encoding non-ordered (nominal) categorical variables of high cardinality

When the dimension is high, all data are approximately at the same distance away from each other. This makes distance-based methods such as k-nearest neighbors less useful if the data are more or less ...
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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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dimensionality reduction of (partly) categorical metadata

I have several datasets, each with with hundreds of samples. I have different metadata for each data set, which contains about 50 variables per sample. Some of this metadata is clearly redundant. For ...
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Discrete Wavelet Transform Time Series

My problem is to cluster some time series together. But due to a huge length I was interested in using some methods to reduce the dimensionality. I was thinking of Discrete Wavelet Transform since the ...
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Search for redundant filters(channels) in CNN

When training a CNN one specifies in each layer the number of channels. In the input we have 1 channel for grayscale image and 3 for RGB image, and then usually the ...
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Utilizing 1x1(x1) convolutions as a learned max pooling (3D)?

I have a semantic segmentation network that ingests 3D images (hyperspectral $(x, y, b)$) and predicts 2D images (semantic map $(x, y)$). This network takes the form of a classic UNet, though it ...
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Is there a way to use UMAP on a weighted digraph?

I know If I have a weighted graph $G$(not a directed graph), I can define a metric on its vertices: $$d(i,j) = \text{weight of the minimum path between vertex i and j}$$ So I think It makes sense to ...
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How do I find the eigenvectors corresponding to the largest eigenvalue of a matrix in scikit?

Im trying to determine the principal component 1 and 2 of a symmetric matrix using sklearn. Id appreciate any help. Thank you.
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Dot product and linear regression

I'm studying PCA and my professor said something about finding the linear regression by doing the dot product of both axis. Could someone explain to me why? The dot product returns a number. What's ...
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What are some good techniques to decrease the size of Image Embeddings returned by CNN model?

I want to extract features from pre trained ResNet model for over 2M data. Problem? Even with the average pooling applied on the last layer's result, it provides a ...
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How to use embedding to reduce features for a regression problem

I’m working on a regression problem in which I’d like to predict demand of different items. I have used holidays as a feature in my model, in a hot encoded format, i.e. I have 11 binary features each ...
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Elimination of features based on high covariance without affecting performance?

I ran into a question where the answer ran me into a big doubt. Suppose we have a dataset $A=${$x1,x2,y$} in which $x1$ and $x2$ are our features and $y$ is the label. Also, suppose that the ...
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Can an Isomap be embedded in a manifold of higher dimension than the corresponding MDS?

I am using the Isomap algorithm to operate a dimension reduction on a distance matrix $M_{dist}$. For a given choice of nearest neighbors k to compute the geodesic distance, I use the following method ...
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MSE errors on autoencoder for dim reduction decreases in a weird patteren and I would love some help to dechyper it

I'm training a denoising autoencoder right now to reduce the dimension of a feature vector I designed of dim 58 to a latent space of dim 10, or less hopefully. I'm having a hard time understanding ...
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Modern methods for reducing dimensions and feature engineering

I am training a binary classifier in Python to estimate the level of risk of credit applicants. I extracted a little over a thousand independent variables to model the observed behavior of four ...
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Hierarchical clustering in R filtering variable

I would like to test the added value of features compared to currently used predictors. First, I checked if features were not correlated to the predictors (volume and intensity) I already use, and for ...
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Neural networks with not-fixed dimension for input and output

I would like to know if it exists a model/method which can deal with input and output of different dimension. For example, let us say that the maximum number of info we could have is 6 features and 5 ...
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Machine learning on graphs

I'm looking for some method/model to help me with my current problem: I have a geometry, consisting of points, and eges. For each point I take information about itself and its neighbours. For now I ...
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Can we use feature selection and dimensionality reduction together?

I have a dataset having about 10,000s of features. The features have a hierarchy inherent to them. I found an algorithm performing feature engineering, taking the hierarchy of the features into ...
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115 views

How do I determine which variables contribute to the 1st PC in PCA?

Given the coefficients of PC1 as follows for each variable (0.30, 0.31, 0.42, 0.37, 0.13, -0.43, 0.29, -0.42, -0.11) which variables contributes most to this PC? Does the sign(+/-) matters or ...
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Do all autoencoders perform dimensionality reduction [closed]

I want to use Convolutional autoencoder to find patterns in data as well as reduce dimensions. Can it be used for this purpose? Moreover, is removal of multicollinear features through autoencoder ...
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Find inputs that give highest variance in output space

I have a relatively simple problem that I think should have a known solution, but that I can't figure out. Any help would be greatly appreciated. Basically, I have a function $f : \mathbb{R}^d \...
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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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Reformer the Efficient Transformer and Image Size Limits

I'm currently trying to use the Reformer: Image Generator with my own dataset. The colab notebook for the model is here: https://colab.research.google.com/github/google/trax/blob/master/trax/models/...
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Reducing the size of a dataset

I am trying to classify gestures. I am using Python's scikit learn library classification algorithms for that. I have collected depth images for this purpose. 200 samples are collected for each ...
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What is major difference between different dimensionality reduction algorithms? [closed]

I find many algorithms are used for dimensionality reduction. The more commonly used ones (e.g. on this page ) are: ...
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PCA and the relationship between number of samples and number of dimensions

I'm performing PCA on a high dimensional dataset: 800,000 samples by 300 features for which my aim is to identify clusters using Kmeans. After keeping only the numerical features that weren't noisy or ...
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Identifying persistent clusters within a series of graphs

The task is to identify persistent clusters, i.e., groups of nodes that "persist" as clusters (tend to form a cluster) in a series of graphs. This is how I approached the problem: I form a ...
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Dimensionality Reduction on Cross Channel EEG data

I'm working on a project predicting seizures events using 10 minute EEG data recordings. This involves identifying the preictal (pre-seizure) phase from the interictal (normal/between seizure) phase, ...
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Factor Analysis with Mixed Data gives many components

I performed Factor Analysis with Mixed Data using PCAmixdata package from R. My dataset consists of 115000 records with 40 features of both categorical and continuous data. I checked the eigenvalues ...
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Factor Analysis with Mixed Data Concurrent Approach with PCAmixdata in R

I am trying to perform Factor Analysis over Mixed Data using R with PCAmixdata package. My dataset is huge with almost 115000 records and almost 40 features of both categorical and continuous. When I ...
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1answer
42 views

How do i perform feature selection for clustering?

I am new to Data science and trying to learn clustering? I have to partition the given dataset into different clusters into customer clusters based on their purchasing habits? How do I select the ...
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Need suggestions on customer segmentation

I have been tasked with performing customer segmentation for a Business to business use case based on customer purchase history. Can experts provide me inputs on how do I proceed with customer ...
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The OLAP (On-Line Analytical Processing) cube with 4 dimensions [closed]

A typical OLAP cube looks like this: As I can see, this cube can work with 2 or 3 dimensions, but what if I have 4 dimensions to produce facts? Should I use star schema instead when having more than ...
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PCA for complex-valued data

I'm quite shocked for encountering this error on PCA from sklearn ValueError: Complex data not supported After trying to fit ...
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Using the input record's distribution as a composite input feature

I have a very interesting dataset that I need to use for doing regression. It is production data from stainless steel production and I have about 290 input features, so I need to start reducing the ...
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PCA vs.KernelPCA: which one to use for high dimensional data?

I have a dataset which contains a lot of features (>>3). For computational reasons, I would like to apply a dimensionality reduction. At this point I could use different techniques: standard PCA ...
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1answer
128 views

Should dimensionality reduction be done before k-means clustering if there are many features?

My data contains over 200 features and over 500 observations. I want to place the observations into a number of clusters based on the features that make them different. There are numerous ideas I ...
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PCA targeted away from some subspace

Is there an existing technique allowing to do PCA maximizing not the variance per se, but the variance away from some direction? Imagine I have high-dim data with two different labels L1, L2 and I ...
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Supervised dimensionality reduction for multilabel data

re there algorithms for supervised dimensionality reduction like Linear Discriminant Analysis (LDA) for multilabel classification? If I understood it right, the implementation of LDA in scikit-learn ...
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How to perform feature selection with Categorical Variables and Continuous Target, provided that data is not normally distributed?

Basically I am trying a use Multi Linear Regression Model to predict the salaries of employees. I have a total of 88 dependent feature from which 19 are categorical and the rest are continuous. I have ...
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Do I use the mean vector from my training set to center my testing set when dimension reducing for classification?

Please let me know if this is the right place to ask this (or if any of my tags are wrong) or if I need to write this any differently. Do I use the mean vector from my training set to center my ...

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