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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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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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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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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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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How effective is Moore Penrose for solving regression problems with overdetermined system of equations?

For regression problems with #Predictors > #observations, I recently read about Moore Penrose (pseudo inverse method) which solves the problem of non invertible matrix in OLS for regression problems. ...
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svd to reduce text matricies

I am having problems with very big texts. after preprocessing each of my docs represented as a matrix of sentences, where each sentence represented as encoded words (each word have a unique vocab ...
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Autoencoder for Dimensionality Reduction - varying result - parameter tuning

I'm not an expert in autoencoders or neural networks by any means, so forgive me if this is a silly question. The problem and steps taken to solve problem are as follows: There exists a data set with ...
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Dimensionality reduction without select components

I would like to use dimensionality reduction algorithm in my pipeline. I have 2k features and I'm using xgboost. My model is rebuilding each day (there are new records that should be involve to ...
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Possible flaw in the MDS method for dimensionality reduction

The MDS (multidimensional scaling) method is used to solve the problem of dimensionality reduction. Basically, it does the following: given $n$ points $x_1,\cdots,x_n\in\mathbb R^d$, try to find a ...
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Low memory error while performing degree 2 polynomial regression on (3000*1835) sized array

I am working on a problem to predict the revenue, a film will generate. Some of the features available in the data set are json collection for the crew, cast which worked in the film. I applied ...
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Potential speedup by applying PCA once on dataset with m rows vs. IncrementalPCA to x batches of size m/x?

I've been working on trying to perform dimensionality reduction on high-dimensional, high-volume datasets (with many rows and columns - around 100,000 - 1M rows and ~500 columns). While the size of ...
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Analogy between Autoencoder and PCA

I know that Autoencoders can be regarded as non-linear generalisations of PCA, but I struggle to understand in depth the analogy between the two. Once PCA has been performed on a function $F(\vec{\...
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Are vectors generated by doc2vec and similar models uniformly distributed?

I have read that vectors in a word2vec model are very much not uniformly distributed and are thought to follow Zipf's law; is this the same for the associated models like paragraph2vec, doc2vec, etc? ...
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find most dense neighborhood of points in high dimensional space

I'm working on a project where I have many high-dimensional points and I want to find the most dense neighborhood of them. Ideally, out of my ~500 points that are each a 4x300 matrix (300 ms time ...
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Magnifying or reducing the size of input groups into a neural network

Say you've got two inputs (X1 and X2) that you want to use to predict Y. You're not sure how important X1 and X2 are for predicting Y, but you assume about even. One-hot encoding is a good strategy ...
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Linear Discriminant Analysis (LDA) before or after k-fold cross-validation?

I have features extracted from a small dataset, would like to reduce the dimensions by using LDA. Also want to do a SVM classification with k-fold cross-validation. My question is: What would be the ...
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237 views

comparison of t-SNE and PCA and truncate SVD

How to compare the trucate SVD ,PCA, and T-SNE? What we can say about features if t-SNE and PCA and truncate SVD digaram is in this figure?
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If in t-SNE digaram of binary classification both classes follow the similar curve what does t-SNE diagram show?

If in t-SNE digaram of binary classification both classes follow the similar curve what does t-SNE diagram show for instance: Figure1 or Figure2
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What is the meaning of 2D vectors?

I keep hearing people say something like: lets say you have a 1 dimensional vector of a person that just has his age. Then you add another dimension which is his height, so you have a 2 ...
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High dimensional data stream summarization and processing

Can anyone recommend a method for summarizing and processing high dimensional data streams efficiently and effectively for anomaly detection? In fact, I investigated the different methods for data ...
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Steps on how to use autoencoders to reduce dimensions

I have a dataset that contains text columns. I have used tf-idf to convert those text columns to numerical columns. I want to reduce the dimension of the dataset since tf-idf creates a multitude of ...

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