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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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K means visualisation after reducing dimensionality with PCA

In clustering ($K$ means, for example) when I have $N$ features and after creating the model (with this $N$ features) to visualize this model I need to reduce this $N$ dimensions into $2$ or $3$ ...
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How to reduce position changes after dimensionality reduction?

Disclaimer: I'm a machine learning beginner. I'm working on visualizing high dimensional data (text as tdidf vectors) into the 2D-space. My goal is to label/modify those data points and recomputing ...
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Deep Q Learning state dimensionality

How important is the dimensionality of each state for Deep Q Learning? I have a set of 15 unique playing cards from a deck of 52 playing cards. A given state is represented by the respective card ...
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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 ...
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Flatten inputs in tensorflow

I would like to flatten a tensor float with a dimension : [?, 12,12,256] into a tensor of dimension: [?, 12,256]. I found that ...
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19 views

Combining scaling, dimensionality reduction, prediction using sklearn pipeline

I would like to use a sklearn pipeline doing this : ( - ) scale the data ( StandardScaler ) ( - ) reduce dimensionality ( PCA ) ( - ) make a prediction with GradientBoostingRegressor() and ...
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23 views

Feature importance after PCA (or other dimensionality reduction methods)

I have text data which I one hot encoded and then used PCA on it (although I'm experimenting with other methods as well, LDA, NMF..). I am using the result of the dimensionality reduction as an input ...
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How to perform Tensor decomposition on a matrix?

I have a dataset that contains 500 rows of songs each of them is having 4 features viz. Singer rating, Music Director rating, Genre (there are 3 genres - Rock, Sentimental, Rap) and Music Company ...
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59 views

Measuring distance preservation in dimensionality reduction

I am looking to compare the distance preserved during dimension reductions for several techniques. I have read some papers on similar topics here and here. For example, I would like to use the ...
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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 ...
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31 views

Convert categorical data in numeric preserve euclidean distance

I m looking how to preserve Euclidean distance with categorical attribute. Ad example, if I have a dataset with attribute of people, Age, weight etc..and i find a attribute "sex" where contain "...
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Out of sample extension for Isomap in Sklearn

If I'm fitting the isomap class with a certain dataset, then I transform with a different one, does that mean that Sklearn is doing out-of-sample extension ? I.e. ...
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What are the cases in which Isomap fails to do a good job?

As above, what is a possible scenario/ dataset/ case in which Isomap fails to do a decent dimensionality reduction?
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47 views

Curse of Dimensionality : How many columns is too many columns?

Say I have a dataset with 1000 columns and 3M rows. I know that this is will definitely suffer from Curse of Dimensionality and that I need to reduce the number of dimensions. But to what extent am I ...
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1answer
27 views

Proper Understanding of Condensed Nearest Neighbor

I have a question regarding the Condensed Nearest Neighbors algorithm: Why am I returning Z, which if I understand correctly, is the array of all of the ...
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1answer
30 views

target in cluster analisys (PCA)

i m doing dimensionaly reduction using PCA. I don't understand why some dataset already had a target ad example in Iris database or other like this (https://scikit-learn.org/stable/datasets/index.html)...
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38 views

PCA in visual Analytics

I m studying visual analytics and i have a theoretical question about this topic. My professor introduced this schema in him slide For connect data to visualisation. Some topic is very easy to ...
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Is dimension reduction helpful to select features for a classification problem?

Let's say I have a data set but I don't know what features are relevant to solve a classification/regression problem. In this case, is it worth/good to use a dimension reduction algorithm and then ...
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Many things behave differently in high dimensional space

It turns out that many things behave very differently in high dimensional space. The below paragraph is picked from a book. I need extra help to understand. The book says, if you pick a random ...
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How to compare Factor and Principal Component Analysis results?

I am currently working on an assignment where I am to perform a comparison of different Dimension Reduction techniques in Python. I am using the Scikit-learn functions to perform PCA and FA. However I ...
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Similarity measure before and after dimensionality reduction or clustering

I have a dataset with 500 000 samples, each sample contains 30 features. The values of the features are in the range 0.0 to 1.0. ...
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How does the hashing trick work in NLP

If I have 1000 words and want to reduce the dimension to 500, does that mean per hash there will be two words on average? In the course NLP specialization on courser, 40 million unique word matrix was ...
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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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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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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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1answer
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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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2answers
171 views

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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1answer
28 views

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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1answer
31 views

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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3answers
120 views

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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1answer
206 views

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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1answer
204 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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302 views

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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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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2answers
271 views

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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158 views

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
221 views

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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91 views

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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2answers
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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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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 am right. Which technique would be ...
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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 ...