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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Result from Step by step PCA implementation differs from `matplotlib.mlab.PCA()`, would be nice if someone can help me finding the source

I was reading this nice article and tried to implement this step by step guide in Python, and then I compared the results using the Python function from the matplotlib library: ...
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24 views

Finding most significant data set within data

Basically, I'm conducting research based on two types of data: Noise levels and the temperature of a room. I've recoded data for 2 days.. I am using the spearman's correlation methods to determine ...
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22 views

Dimension reduction for likert questions, cronbach alpha

I need some input on how to proceed with my data. I have collected data from household survey on 180 sample to find out the importance given to the attributes in a residential location decision. The ...
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81 views

Machine learning techniques for spam detection, and in general for text classification

I am going to configure a system for spam detection. What I have is a dataset of labeled (spam/not-spam) strings containing, mostly, sentences. I have a background in machine learning techniques, but ...
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Comparing isomap residual variance to pca variance

I am using R princomp function (from stats package) to run a PCA on a data set and I want to compare its output to that of the nonlinear dimensionality reduction method ISOMAP, which I am using under ...
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33 views

Define a hypersherical neighborhood around a target point with radius r'

This is a homework problem that I need some help with. QUESTION: We assume that N data points are uniformly distributed in a 100-dimensional unit hypersphere (i.e. $r = 1$) centered at the ...
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55 views

Linear model predictor selection. Which method to use ?

From what I understand, there are 3 main types of predictor selection method for linear models, namely, 1 Subset Selection, 2 Shrinkage and 3 Dimension Reduction. The subset selection includes the ...
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31 views

Combining variables

I'd like to combine several variables into one variable. Here is some context: Let's say I have two variables Red.Beads and ...
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29 views

When accuracy in the cross-validation process less,is reducing the features a good idea?

I am doing a project for classifying the presence of cars/bikes in an image.I have extracted the features from the images(data-set of cars and images not belonging to that of cars) and applied K-means ...
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Performing PCA with only a distance matrix

I want to cluster a massive dataset for which I have only the pairwise distances. I implemented a k-medoids algorithm, but it's taking too long to run so I would like to start by reducing the ...
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Creating consensus from multiple methods of measuring the same entity with some missing values

Imagine we have C cars and D drivers, and each driver takes a large subset of these C cars in order to test the rate of fuel consumption for some fixed amount of fuel (let's assume that the number of ...
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two inversly correlated indepent variables…how to combine?

I have a few instances where two independent variables are almost perfectly correlated inversely usually in such an instance i would remove one in this instance the two variables each comprise: ...
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Multidimensional scaling of variables with multiple sub-features?

Let's say I have a year's worth of magazine issues (January, February, March, etc), and I want to visualize the differences among them. The classic example of multidimensional scaling (MDS) would have ...
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93 views

dimension reduction of discrete numerical data

I have a bivariate discrete numerical dataset and would like to reduce its dimensions to a single variable. A 9 x 8 table of counts of the (x,y) data values is: ...
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29 views

Fitting a parameterized 2D distribution

I frequently deal with datasets that rely on a binary classification based on a Pulse-Shape-Distribution (PSD) discrimination value. This is based on a 2d fit of the PSD value vs the amplitude of the ...
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30 views

Representing 140 items with 40 items

I wanted to reduce a large number of items down to a smaller number (e.g. 140 items to 40), and then show that these 40 items can accurately predict two related variables. I have attempted CFA ...
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64 views

The curse of dimensionality? (linear SVMs)

How do you know whether you suffer from it? Let's suppose I have a 2 class problem - 2000 training examples and 30 features. While it works good for the most part, sometimes I get edge cases that ...
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129 views

Regression testing after dimension reduction

I have a 12 item Likert scale for my predictor variable [IV], and a 9 item Likert scale for my dependent variable [DV]. I used SPSS and did factor analysis on both scales, and found that the IV had 3 ...
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110 views

Advice/literature on combining items with different response scales into composite scales?

Let's say I have some self-report items measured on a 5-point Likert scale (Strongly Disagree to Strongly Agree) and other items measured on a 4-point Likert scale (Never, Rarely, Sometimes, Often). ...
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51 views

Dimension Reduction using PCA and Random Forests

I an using scikit-learn as a toolset. I have 1K features as candidates and am trying to reduce the feature set as I believe the majority is noise (but am not sure). I wanted to somehow automate ...
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32 views

Steps to follow for correspondence analysis when each brand is not shown to every respondent

I want to understand the steps followed for correspondence analysis when each brand is not shown to every respondent. Till now I used to assign a number (proportion) to each brand for each attribute ...
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198 views

time series dimensionality reduction

I have a call center data (such as one below) that has call data collected every 15 minutes. For a day the periodicity is 96 and for a week the periodicity is (7 x 96 = 672). If I would like to ...
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59 views

Kernel PCA with an SVD algo

Suppose that I have a great algo for calculating the SVD and I want to do Kernel PCA. It is possible to first apply the Kernel function to my data and then run the SVD algo on the transformed data?
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Explain steps of LLE (local linear embedding) algorithm?

I understand the basic principle behind the algorithm for LLE consists of three steps. Finding the neighborhood of each data point by some metric such as k-nn. Find weights for each neighbor which ...
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The first principal component becomes irrelevant

I did run PCA on 17 quantitative variables in order to obtain a smaller set of variables that is principal components to be used in supervised machine learning for classifying instances into two ...
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Relationship between dimentionality reduction and clustering algorithms

I've got bit confused about dimensionality reduction and clustering . whether all clustering algorithms (k-means, affinity propagation, spectral clustering,...) do kind of dimensionality reduction ?
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Feature selection on microarray for supervised classification

I'm studying the task of feature selection on biological microarray, thats is high dimensional dataset (thousands of features) with small number of data points (lees than one hundred). This feature ...
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Non-negative Matrix Factorization - Basic Question: Cluster Assignment

Given the result $V_{m\times n} \approx W_{m\times k} \cdot H_{k\times n}$, where columns of $V$ are data points and $m$ is data dimension, what is the function by which you assign the data points to ...
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58 views

Project feature vectors into 2D plane for visualization

I would like to project my observations which consist of more than 2 variables into a scatter plot. Some time ago I saw an R package that does this by reducing the dimensions (possibly using PCA and ...
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What is exactly code vector and quantization vector of self organizing map?

I am trying to understand code vector in self organizing map. Could anybody explain me intuitively what it is exactly?
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54 views

The size of my reduced data set is greater than the original

I have an original data set with a number of features N equal to 135 and a number of rows equal to 32000. The last column of the data set ( column 136 ) can take either -1 or 1 depending on the class ...
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66 views

Is it feasible to use t-SNE to reduce a dataset to one dimension?

Is it feasible to use t-SNE to reduce a dataset to one dimension? Suppose that I have a matrix, $X$, can I reduce it to a column vector, $Y$ with t-SNE? Suppose that $X$ has 100 columns, how much ...
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54 views

Why do PCA and Factor Analysis return different results in this example?

The following question is about an example from "The Elements of Statistical Learning" by Hastie, Friedman and Tibshirani $X_1 = Z_1 $ $X_2 = X_1 + 0.001 * Z_2 $ $X_3 = 10 * Z_3 $ Where $ Z_1, ...
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Why the maxStages argument in biglars.fit does not work

Why doesn't the biglars.fit function work when maxStages is specified? I've tried multiple values and multiple ways of casting $y$ but it doesn't work. ...
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Unsupervised Dimensional reduction for mixed data types

I have a data set with about 50K rows and 100 columns. You can consider every row to be representing one restaurant. My goal is to calculate dissimilarities between all the restaurants - Gower's ...
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101 views

Question about Eigenfaces (PCA applied to facial images)

I've been reading up a bit on eigenfaces. I think I understand the basic concept of it - vectorize a set of facial images then reduce the dimensionality of the images using PCA. What I don't really ...
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33 views

Reducing data size to cross correlate with another data set?

I have three matrices A, B, C. A is a matrix of 200 X 32. There are 2000 such different A matrices which make up the B matrix. B is a matrix of 2000 x A. That is there are 2000 x 200 rows in matrix B ...
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Deep learning with $p \gg N$ and on multi-modal and heterogeneous data

I work in a problem where: The input data is a collection of time series from different sensor modalities, where each sensor comes with different acquisition rates and dynamic ranges The number of ...
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105 views

More complicated classifier vs data preprocessing

For example if we have two options to use non-linear classifier like SVM with kernel or use linear classifier like linear SVM with data preprocessing like some non-linear dimensionality reduction ...
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99 views

Ideal number of variables for PCA analysis

I working with a dataset of around 4000 variables. I decided to carry out a PCA analysis for the data, but I am not quite sure about the suitable number of variables I should include in the test. ...
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71 views

k-fold feature selection

I have a data set with 20 K variables. I have tried to select some features via Boruta and FSelector but I could not achieve ...
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135 views

Perform PCA. Extract PCs. Can one then tell what the most important _original_ features were, from the PCs?

Suppose that you have 1000 features, and a data set made up of say, 50,000 points. Suppose then that we perform PCA, and we extract the top 5 PCs, since they explain 99.99 percent of the variance, and ...
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79 views

Variable reduction by means of ANOVA?

I have a typical problem with several variables and a large amount of data which are not important right now. The goal of the study is to relate variable $Y$ with variables $X_1,X_2,...,X_n$. I have ...
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65 views

Things to consider before summing binary variables to create a total score

I would like to reduce the amount of data I have before conducting an analysis. My data consists of several sets of questions assessing comprehension of various topics (e.g. 4 questions assess ...
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57 views

Differentiation and dimensionality reduction

I have a matrix $x=N\times M$ of $N$ data points, where each one has $M$ features. Also, $y$ is the binary labels vector. In my case, $N$ is much smaller than $M$, so before running a classifier like ...
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53 views

Reduce dimensions on a data set and its clusters' centroids

I am building a small application to calculate clusters from some input set (a n x p matrix). After I finish running the algorithm to get k clusters I also obtain the centroid of each cluster (a k x p ...
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238 views

Interpreting plot of PCA results (from 3 to 2 dimensions)

I'm having trouble understanding how to interpret/explain the end result of dimensionality reduction via PCA. Namely, I've attempted to code up a simple example in R but can't really say what ...
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1answer
86 views

How do SOMs reduce dimensionality of data?

This is a problem with which I have been grappling with for days. From my research on self-organizing maps, I know that a common feature of self organizing maps is to reduce the dimensionality of ...
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72 views

Computing similarity in LSA

I have a question regarding Latent Semantic Analysis (LSA) introduced in http://lsa.colorado.edu/papers/JASIS.lsi.90.pdf‎ . On page 14, schemas for calculating similarities between different types of ...
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How to reconstruct the original data when using manifold learning?

I'm using Isomap to reduce the dimensionality of my data. Isomap use geodesic distance rather than Euclidean distance to perform a MDS. Now I want to reconstruct my original data with a lower ...