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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How to perform a 1-way ANOVA right after One-Hot-Encoding

I am at the phase of dimensionality reduction. I am trying to figure out which categorical columns I should keep for my model and which I should discard. Because some of my categorical columns have ...
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How to automate ANOVA in Python

I am at the dimensionality reduction phase of my model. I have a list of categorical columns and I want to find the correlation between each column and my continuous ...
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1answer
21 views

Eigen Decomposition of Data Matrix for PCA

In PCA we Eigen decompose the covariance matrix, not data matrix, Is it because most data matrices are non-square. If they were, isn't is correct to eigen decompose data matrix than the covariance ...
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48 views

When I should use PCA?

I have a data set with 60000 rows and 32 columns. I want to use SVM (with some more constraints that make it more complicated)and I think 32 columns are too large. So I decided to use PCA. But when I ...
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25 views

Dimensionality reduction based on value of a variable

I have a dataset including 100k high dimensional data (e.g. houses in LA) (dim=100, e.g. house parameters like area, distance to downtown, etc.). Below is the 2-component PCA representation of the ...
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Does it make sense to train an Autoencoder for Dimensionality Reduction using Mini-Batch Gradient Descent?

I want to reduce the dimensionality of a dataset using a stacked Autoencoder. The size of the dataset and the computing power at my disposal make it very difficult to train the Network using simple, ...
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Design / Choice of Autoencoder to classify temporal pattern in images

Suppose I have a temporal stack of images of shape $m \times n \times k$ where shape of each image is $m \times n$ and $k$ represents the temporal dimension. In this context, I am trying to detect and ...
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40 views

Implication of a dominant Principal Component in PCA analysis

I need help, are there any practical implications of a dominant principal component. For example, if of three PCs, PC1 explains almost 100% of the variance in this dataset, What does this mean in ...
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2answers
32 views

Difference between LASSO penalty in neural network and just LASSO regression

I wonder whether those two have any significant differences. I think in neural network, the lasso penalty put on the loss function makes the model simpler and introduces more sparsity by ...
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1answer
53 views

How to calculate compression ratio when using autoencoder in neural network

For example, if I use an autoencoder to compress a 1000 dimensional data set to 25 dimensions. Is the compression ratio is 40:1? Other info: The dataset contains 5000 samples. 2 million parameters ...
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64 views

What is the difference between and Embedding Layer and an Autoencoder?

I'm reading about Embedding layers, especially applied to NLP and word2vec, and they seem nothing more than an application of Autoencoders for dimensionality reduction. Are they different? If so, what ...
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Feature selection or Dimension reduction in unsupervised learning

I'm trying to do Embedded clustering using kmeans. This is customer data, so it involves a lot of sentences, so I'm using the universal sentence encoder before clustering. But I should be doing a ...
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How to scale or standardize data that is mostly 0 (ranges from 0-1)?

I am relatively new to data science and big data munging in general. I currently have various columns of data that range from $0-1$, but most of the values in each ...
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Multiclass classification with high number of classes, high number of features and small sample size

I am working on a biology related dataset with over 300K features, and I only have about 5K samples. I want my model to classify many classes. For this problem in particular the class is age. Each age ...
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How do we visualize data in hierarchical clustering?

Can anybody tell me how to do visualization when applying hierarchical clustering to data with more than 2 features? Do we need to do dimensionality reduction before each clustering?
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Is there a representation of the separating hyperplane in t-sne?

I have used t-sne to visualize a set of images which I have used for training a binary classifier. Let us assume that the binary classifier is trained to detect cat(1) vs. no-cat(0). I have used the ...
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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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25 views

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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1answer
29 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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33 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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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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67 views

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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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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58 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
29 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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39 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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4answers
49 views

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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94 views

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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196 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
30 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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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
151 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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38 views

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 ...