A sparse matrix is a matrix where many of the elements are zeros.

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Is large scale PCA even possible?

The principal component analysis (PCA) algorithm assumes that columns of an input matrix have zero mean. This can be achieved easily, but when the input matrix is sparse, the centered matrix will now ...
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62 views

Finding pattern from sparse matrix

I have a large sparse matrix which represents whether the action is happened or not. Each columns represent each action. Each row represents time. The data is collected for every 15min. Zero in the ...
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8 views

Quantile or distribution estimation for continuous variable from sparse matrix

I'm not sure where to start and desperatley need help. I've got a somewhat sparse data set and I'm trying to do either a quantile estimation or a distribution estimation for one continuous variable. ...
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34 views

How to go from sparse matrix to linear regression model (using SVD)?

I am trying to replicate the Kosinski, Stillwell, & Graepel (2013) study about predicting private traits and attributes from Facebook like data for study purposes. First I have admit, however, ...
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27 views

Is it possible (and if yes how) to retain a sparse matrix after normalization?

I was wondering whether given a sparse matrix it is possible to retain a sparse matrix after removing certain global effects. Let me demonstrate the following: Given a data set $X$ with dimensions $m ...
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37 views

Kernel K nearest neighbours with sparse data

I have a big sparse matrix (around 5 million of lines, 20 000 predictors), and I would like to run a kernelized k-NN on it. However, I don't know how to scale the data properly. So far, I have scaled ...
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9 views

Can I run an SVM on sparse temporal data without a regular time interval?

I have data of occurrences with timestamps that could be days or months apart. I'd like to enter the values natively as follows. Are there any SVM algorithms that can support such an input? day 1: ...
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17 views

Robust Sparse K Means clusters and valid index

I used the robust sparse k-means for clustering my dataset and I would like to calculate some distance-based statistics for evaluating my results. Should I compute them on the dissimilarity matrix ...
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5 views

Valid values for nvecs sparseDecom2

I hope this is the right place to ask, and that someone would know the answer I am searching for the optimal sparseness value to solve a sparse regression problem (using MRI data) that involve ...
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31 views

Feature scaling for non-negative sparse data

Imagine you have many observations on which you want to run a classification algorithm. Each observation is characterized by a matrix of non-negative values. For all observations 90-98% of the values ...
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80 views

What's the best (Google chart) visualisation for displaying sparse timeline data across thousands of “columns”

I am trying to visualise a sparse dataset but am finding it hard to fit it into the standard categories of charts. I'm a developer building with Google Charts and I really want to stick with that ...
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106 views

Does the opposite of nested cross-validation make sense?

I'm asking the question from a machine learning point of view. I have a dataset with relatively high sparsity, so if I use nested cross-validation for my feature tuning and evaluation, that is tune ...
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15 views

commonalities and differences among groups in a matrix (based on features)

I have a (rather sparse) matrix with 7 columns and 66 rows. Each column represents a user group, each row represents a feature, and each cell represents a weighted value (see note below). I'd like to ...
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54 views

How to input sparse feature

In theano everything is symbolic, so how to input sparse feature in , for example, neural network? The setting is: the task is text application. the input is a mini-batch. Since theano sparse module ...
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20 views

Is there any reason that cross correlation would perform well or poorly on sparse binary arrays?

I am using matlabs xcorr to correlate simulated photon count data that has some Gaussian random noise set on top of it and it is working fine when the average value in the arrays is greater than one ...
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16 views

Minimum sample size required for sparse PCA

What is the minimum sample size that we need for filtering variables using sparse principal component analysis (sparse PCA, SPCA)?
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28 views

Instances of sparse covariance matrices

I am trying to find large datasets with inherently sparse covariance matrices, to be tested with our algorithm. Basically, we will take the sample covariance matrix and enforce some structured ...
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24 views

Sparsity of the matrix

We got a matrix of 500 users and 30 tracks. This matrix is complete full (it means every user rated explicit all 30 tracks). Every row is a combination of user id, music id and rating. Every user + ...
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86 views

sparse covariance/correlation thresholding

In our project, we would like to do some optimization on sparse matrices. The idea is to scrape massive amounts of data, form a covariance/correlation matrix, and form a sparsity pattern basically by ...
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33 views

What are the techniques to deal with classifying sparse categorical features?

Suppose I have a group of features each one is sparse with a few number of values (1-10) what are the required preprocessing steps required to avoid degradation of the performance of the classifier ...
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103 views

How can I run ordered logistic regression on a large sparse matrix in R

I have a sparse matrix X, 970283x9511, with 970283 documents and 9511 features. I have a vector y of length 970283 corresponding to a rating 1-5. I know of the glmnet package, which has binomial and ...
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75 views

Problems using pcr (from pls library) in R with large number of qualitative variables

I'm trying to classify a variable into either 0 or 1, using 50 factors, with a sample size of 2000. 25% of the dependent variables are 0 and the rest are 1. Of these factors, 30 are categorical. I've ...
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77 views

Handling sparse document term matrix

I am working with a corpus of several thousand documents (41,732) however the documents tend to be short (the median number of terms per document is 3) resulting in a sparse document term matrix. ...
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22 views

Spectral norm of a sparse Gaussian matrix

Suppose $G$ is an $m \times n$ matrix such that each entry of $G$ is a standard normal variable. We know that the spectral norm of $G$ scales as $\sqrt m + \sqrt n$. Now, given a set of indices $S$ ...
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15 views

Preprocessing for dictionary learning

What are the pre-processing steps on data before dictionary learning,say using KSVD? Do we have to standardize the data first? Thanks.
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20 views

Individual factor significance in multilevel sPLS-DA

I recently was asked by reviewers to "include p-values" with my multilevel sparse partial least squares analysis. In brief, I have a nested design with two factors, say treatment and sampling region. ...
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46 views

How to convert the objective function to canonic form of sparse coding?

As we know the conventional sparse coding problem (LASSO) is: $\min_{\alpha} \| X-D\alpha\|_F^2 + \lambda \|\alpha\|_{1} \tag{1}$ where $X$ , $D$, and $\alpha$ are data, dictionary and coefficients ...
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Generate Symmetric Positive Definite Matrix with a pre-specified Sparsity

I am trying to generate a correlation matrix $p\times p$ (symmetric p.s.d) with a pre-specified sparsity structure (specified by a graph on $p$ nodes). The nodes that are connected in the graph have ...
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91 views

Calculating means and confidence intervals when most data points are 0

I am looking at data set that has four groups. In each group, the data is mostly, 99+% of time, composed of zeros, but, when it is not zero it can be any float number (e.g., 0.01 to 921.2, with most ...
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182 views

How to build a predictive model with a billion of sparse features?

I am making a model to learn a dataset which has a big feature number and sparse samples (I am planning to use logistic regression). The feature number can be as big as 1,000,000,000. It is sparse ...
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183 views

Kernel SVM on sparse data

I have a sparse dataset where a lot of the columns (features) contain mostly zero values. Class labels are multiple discrete categories (10 classes to be precise). I'm wondering if this should trouble ...
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103 views

logistic regression with sparse predictor variables

I am currently modeling some data using a binary logistic regression. The dependent variable has a good number of positive cases and negative cases - it is not sparse. I also have a large training set ...
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26 views

Small sample size : dealing with bootstraping for linear or nonlinear multiple regression

I am wondering to heal my ignorance from your experiences or your modeling knowledge. I have many matrices of quantitative variables, let me start with three matrices of proportions.To express ...
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49 views

Sparse PLS: algorithm for variable selection and model fitting

In the spls package in R (based on the manuscript by Chun and KeleĊŸ [1]), there is a separate specification for the variable selection and fitting in the main function, ...
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What guidelines should be followed for using Neural Networks with sparse inputs

I have extremely sparse inputs, e.g. locations of certain features in an input image. Further each feature can have multiple detections (not sure if this will have a bearing on the design of the ...
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394 views

Cross correlation for very sparse binary data

I have a very large (5271159x60) sparse (~2.5%) binary matrix, and I'd like to calculate the cross correlation between each of the columns (sensors) for a series of lags from -10:10, which would give ...
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27 views

cross-validate hierarchical model for binomial data that is often sparse

I have binomial data (e.g, 130 successes in 4000 trials). In many, if not most, cells of interest, there were few trials and thereby few successes (e.g., 0 successes in 35 trials, 1 successes in 18 ...
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103 views

Euclidean distance with sparse and high dimension data

I have texts for a bunch of objects. From each text, I removed the stop words, and took each word as an attribute of the object. I then gave each word a rating based on sentiment analysis, so that the ...
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59 views

Reduction of sparse features for machine learning

I'm trying to use a 1D histogram as a feature for machine learning. A histogram instance can be very sparse and the range of its bins is theoretically unbounded. Moreover it is expected that non-zero ...
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101 views

Tanh activation function and sparsity constraint

According to Lecun's paper "effient backprop" [1] the tanh activation function should be preferred over the logistic activation function for the hidden units in neural networks. For the tanh units ...
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86 views

The name data sparsity in different applications

I am recently surveying the techniques or algorithms which handle the data sparsity problems in various fields. And I find quite similar name "data sparsity" or "sparse data" is used including the ...
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25 views

How to do if the most training data is sparse

Consider a problem like this You have a customer profiling application(say classic telecoms data) You have customer transactions(lots of them) you want to find rules There is a data element which is ...
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38 views

Using an odds ratio when data is sparse

Suppose I have around 20 exposures that potentially affect an outcome and I want to see which exposures have bigger impacts on the outcome. So I want to calculate each exposures' odds ratios by ...
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1answer
25 views

Utilizing A Correlation Matrix Derived from a Sparse Matrix

I have large correlation matrix in Excel that I'd like to use to inform my choice of explanatory variables in a multiple linear regression model. One problem is that the initial data was very sparse, ...
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192 views

LibFM sparse data format

Does the order of columns in the the sparse format matters in case of libfm ? Can I list the non zero components of X in libfm in any order in a row.
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49 views

Literature for Cross Validation on Sparse Data?

I've read a lot about Cross Validation to estimate prediction error, specifically for selecting the number of components in a PCA model (I'm not doing SVD/PCA, but it's very similar), but I can't find ...
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508 views

Clustering algorithms for extremely sparse data

I am trying to cluster an extremely sparse text corpus, and I know the number of clusters (my data is the title and author list of scientific publications, for which I already know the number of ...
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157 views

regression algorithms which work with sparse categorical predictors

I am working with a very sparse problem with a large number of categories per feature and I am currently looking for existing machine learning regression algorithm implementations which can either ...
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2answers
573 views

Algorithm and R implementation of sparse PCA

Does anyone know where I can find an algorithm, as well as an R implementation of it, to carry out sparse principal component analysis (PCA)?
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53 views

Statistical Practices Using Sparse Data: Methods for Approximating Standard Deviation

Suppose I know that for a discrete, non-negative r.v. $X$ that $X | X \geq 1$ has $\mu = 3.3$ while when $X \geq 0$ has $\mu = 2.1$. That is, the subset of the population that already has a value ...