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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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Dimensionality reduction categories

According to what I found, dimensionality reduction has two types feature selection and feature extraction . In feature extraction, we find PCA, LDA ,LLE , ISOMAP, etc.. In other works i find random ...
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Dimension reduction for data with categorical features [on hold]

I am trying to reduce the dimensionality of the dataset. My data contains a large number of categorical features which are creating problems with the dimensionality reduction techniques I am using (...
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Given a 12x12 binary image (only black and white pixels) what is its dimensionality? And how can I define dimensionality of a data space?

Suppose I have a grid 12x12 of pixels that can be only black or white. I can't understand if the dimensionality is 2 or 3. I mean... Is dimension given by 12x12 or 12x12x2 ?
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When projecting data with UMAP, should I use only the samples I need projected or the entire dataset?

I have a neural network that maps my data samples to a 64-dimensional embedding. I wish to visualize a few of these embeddings (between 30 and 600) through a 2-dimensional projection, and I plan to ...
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Dimentionality reduction with suggestion on logically combining features that are better predictors when combined together?

Anyone knows about a code that performs dimentionality reduction with a suggestion on combining features that are better predictors when combined logically together instead of them being combined ...
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How to append a subset of data that has PCA applied to the main dataset

I have a dataset that consist of users profiles and books they read. I have a 2nd dataset that contains description/information on the books. This dataset has 30 features, which I have applied PCA to. ...
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Dimension of the manifold on which my data sits

Suppose that I have data points, in the form of vectors with binary entries. We create a metric space, or Vietoris-Rips complex, using the Hamming distance between the data points. I would like to ...
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Training on continuous data for profile regression

I have a large data set consisting of millions of 1-dimensional profiles. The profiles themselves are arbitrarily complicated continuous functions, $f(x)$, each bound from $0 < x < 1$. These ...
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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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multivariate clustering, dimensionality reduction and data scalling for regression

I have a dataset with approximately 20000 observations consisting of 40 independent and 1 dependent variable. My initial objective is to develop a model that will predict the dependent variable. I ...
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Change values from nominal to numeric

I want to change the values of the class labels from nominal into numeric. e.g if the values of a class are {iris-setosa,iris-virginica,iris-versicolor} i want to make them {0,1,2} so the instances ...
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Extracting Useful features from large convolutional layers

I have been training a convolutional neural network on emotion detection. Now, I would like to extract features for my data to train an LSTM layer. In my case, the top convolutional layers in the ...
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How to choose PCA or KernelPCA a priori?

I am learning about dimensionality reduction and I understood that one of the most used techniques in ML is PCA. If I understood correctly, I use PCA whenever I want to reduce the number of features ...
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Spectral clustering with heat kernel weight matrix

I am studying normalized graph cuts, and one of the way to define weight matrix is using heat kernel, which is $W_{ij} = e^{\frac{−∥x_i − x_j∥^2}{σ^2}}$. I want to ask: what's the meaning of sigma? ...
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Differences between applying KMeans over PCA and applying PCA over KMeans

Short question: As stated in the title, I'm interested in the differences between applying KMeans over PCA-ed vectors and applying PCA over KMean-ed vectors. Long question: Let's suppose we have a ...
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Can anyone explain me the difference between Factor Anaysis and PCA?

Is PCA and Factor Analysis same? Both are used for Data dimension reduction but theoretically I am not able to find the difference between them? I did FA in SPSS to reduce number of variables in my ...
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Do I need to fit on train data for truncated SVD and then transform the test data on fitted train data?

Regarding truncated SVD(single value decomposition) do I need to fit on train data and transform the test data on fitted train data? or can I fit on test and transform on test fit?
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How to handle large number of features in machine learning?

I try to do normal classification on high dimensional traditional columnar data (several hundred columns). The features are of different type. In this case, it's clearly out of question to examine ...
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Efficient dimensionality reduction for large dataset

I have a dataset with ~1M rows and ~500K sparse features. I want to reduce the dimensionality to somewhere in the order of 1K-5K dense features. ...
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What does it mean by “t-SNE retains the structure of the data”?

I was learning about t-SNE when I was told that t-SNE retains the structure of the data in the embeddings. What exactly does this mean ? How does the algorithm achieve this ? So far I have ...
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Are dimensionality reduction techniques useful in deep learning

I have been working on machine learning and noticed that most of the time, dimensionality reduction techniques like PCA and t-SNE...
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Feature selection after performing PCA

I have a data set with 57 variables on which I am performing PCA. The PCA returns me a list of principal components, each of which is in turn a list of the loadings to be placed on my variables in a ...
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How to use SVD to reduce the dimension of test data which is not available at the time of SVD?

Usually, when both train and test data are available in the beginning, a dimensionality reduction such as Singular Value Decomposition (SVD) can be applied on both of them as one matrix. The reduced ...
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Is it possible to compute residual variances of Laplacian Eigenmaps, Diffusion Map and t-SNE to have a scree plots as in PCA?

I will like to do comparative analysis of PCA vs. Laplacian Eigenmaps, Diffusion Map, and <...
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58 views

Reconstructing original data points from t-SNE output

I have been trying to understand t-SNE for a while now and I have this very basic question on the comparison of PCA and t-SNE, on which I would really appreciate some help. In case of PCA suppose the ...
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Distributed PCA or an equivalent

We normally have fairly large datasets to model on, just to give you an idea: over 1M features (sparse, average population of features is around 12%); over 60M rows. A lot of modeling algorithms ...
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Visualize similar looking words plus time related feature

I would like to visualize a high dimensional space consisting of words, the way the look and when were they more used. For the similarity I use various ranges of ngrams on the letters (this ...
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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 ...
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How does multidimensional scaling achieve information reduction?

How does multidimensional scaling achieve information reduction? My notes only give that it transforms the data points into a new coordinate system, $\mathbb{R}^n \rightarrow \mathbb{R}^2$. But how ...
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Accuracy reduces drastically when using TruncatedSVD with hashingvector

I have around 0.8 million product description with categories. There are around 280 categories. I want to train a model with given dataset so that in future I can predict Category for the given ...
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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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1answer
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Discarding correlation among inputs in a neural network

I am working on a problem with 4 inputs and 1 continuous output variable. The sum of all values of the 4 input variables is always 1. a1+a2+a3+a4=1 So, they are correlated. My question is: ...
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Principal Component Analysis and abnormal data

I know that PCA is good in differentiating between anomalies and normal data and it helps to differentiate between them when it tries to transfer the data to another dimension. I mean it can somehow ...
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How to create interactive plot of thousands of images as output of t-SNE?

I have many images that I want to plot as a result of running t-SNE and I want to be able to interactively explore them. matplotlib does not allow enough interactivity to explore, and plotly is too ...
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Reduce dimensionality of ResNet50 extracted features

I've extracted features from the unlabeled dataset of 50k images using a pretrained ResNet50. Specifically, features come from the ...
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Manifolds isometric to a sphere

What are some examples of manifold datasets that are isometric to a sphere? I see that there are plenty of them which are isometric to a plane (eg: swiss roll, s-curve, some image manifolds: http://...
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Feature selection vs Feature extraction. Which to use when?

Feature extraction and feature selection essentially reduce the dimensionality of the data, but feature extraction also makes the data more separable, if I'm right. Which technique would be ...
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1answer
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Performace of Fischer projection as dimension reduction compared to other LDA methods

How is the performance of Fischer projection compared to other LDA methods of dimension reduction? I thought that Fischer projection was a great method of dimension reduction by maximizing class ...
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How to use pca results for linear regression

I have a data set of 11 variables with allot of observations for each one. I want to make linear regression on the variables with the observed $\vec{y}=\alpha +\beta*\vec{X}$ when X is matrix. I'm ...
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71 views

Applying machine learning algorithms to subset of attributes in dataframe

I have this huge mixed data set consisting of both numerical and categorical attributes which upon OneHotEncoding results into a data set with very high dimensionality. Is it wise to apply machine ...
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How to augment data set after dimensionality reduction?

Let's consider a dataset of labeled images ready for a classification task. I'd like to augment my data set, by applying shifting and rotations for example. The problem is that the dataset alone fills ...
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Optimal dimensionality reduction methods for large square, mirrored, matrices (MxM)?

I have 10,000 x 10,000 (up to 100,000 x 100,000) matrices. The matrices denote pairwise similarity between elements of a same sequence. M(1,2) is the similarity between points 1 and 2. M(500,4250) is ...
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953 views

Applying dimensionality reduction on OneHotEncoded array

I have a really large data set with mixed variables. I have converted categorical variables to numerical using OneHotEncoding and it has resulted in more than a ...
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1answer
52 views

Non Deterministc Dimensionality reduction [closed]

could you please suggest me a nondeterministic algorithm for dimensionality reduction except t-SNE.
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120 views

Including the dependent variable in your data to perform principal component analysis?

Let's say you have a data set with GPA (dependent variable) and Amount of alcohol, Amount of study, IQ, and SAT score as the independent variables. And you want to perform the principal component ...
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103 views

How to choose variables for regression

I have a dataset of long/short equity hedge funds returns and their associated benchmarks (market indices). I need to form multiple regression on the fund returns using the benchmarks returns as ...
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How can I reduce dimention using Deep Belief Network?

I have a database with size 1000×500, I reduced it to 1000×50 using KPCA. But How can I reduce the data to make it 1000×50 using Deep Belief Network? I have the following parameter value at the last ...
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1answer
374 views

Which dissimilarity/similarity measure use after a dimension reduction ( PCA / AutoEncoder / … )?

Each problem required its own similarity/dissimilarity measure. Imagine we are dealing with dataset composed with vector of real. I suppose that we mostly use the euclidean distance especially in low ...
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What is local-NMF? How is it better than original NMF?

I am reading this paper, but don't really understand. Do the words "part-based" or "local" for non-negative matrix factorization (NMF) mean that the algorithm aims to factorize some specific parts ...
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Some questions about feature hashing in the context of document classification

I'm trying to understand feature hashing, specifically in the context of document classification. I'm under the impression that it is useful because: it allows us to easily deal with 'new' words/...