Tagged Questions

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

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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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1answer
55 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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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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1answer
50 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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17 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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1answer
17 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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23 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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9 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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16 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
18 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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1answer
42 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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37 views

Sparse ELM vs SVM

What's the difference between SVM and Sparse Extreme Learning Machine with Gaussian kernel proposed in the following paper:http://www.ntu.edu.sg/home/egbhuang/pdf/Sparse-ELM-IEEE-T-Cybernetics.pdf As ...
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1answer
29 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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1answer
121 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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0answers
65 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
104 views

Sparse principal component analysis

Does anyone know where I can find an algorithm (as well as an R implementation of it) to carry out sparse principal component analysis?
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0answers
19 views

Heavy tailed distribution results in sparse random variable?

I often come across that, in Bayesian inference, to encourage sparseness, we should choose a heavy-tailed probability distribution as prior. AFAIK, in heavy-tailed distribution, it's more likely for ...
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1answer
40 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 ...
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145 views

Compute Shannon entropy between every row of a large, sparse matrix

I have a sparse, binary matrix of user (rows) and items (columns). Each element of this matrix is either 0 or 1: ...
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1answer
172 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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1answer
107 views

Selection of sparse principal components

Does anyone have experience with approaches for selecting the number of sparse principal components to include in a regression model?
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0answers
67 views

Clustering time-shifted sales time-series

I need to perform clustering and classification of time series of weekly sales of different products. My data are weekly sales of different products in differest areas (stores). The challenges on this ...
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47 views

Active set methods for group lasso?

When the solution is extremely sparse, probably the fastest method for solving LASSO regression is least angle regression, which starts from an all-zero solution and adds nonzero elements to the ...
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34 views

Random classification forests for extremely sparse response variables

I have a response variable that can be $A,B,C$. It is very sparse, meaning 99% of the sample is $B$ and the rest is approximately evenly divided between $A$ and $C$. How do I predict this variable in ...
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1answer
91 views

Sparse PCA/Dictionary learning when the features are extremely sparse?

I am trying to do sparse PCA/dictionary learning, that is decompose a matrix $X\approx UV$ where the loading matrix $V$ is sparse, usually enforced with an $\ell_1$ penalty (the difference between ...
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37 views

Optimal to sub-optimal sampling patterns

In the compressed sensing (CS) framework an incoherence property $\mu$ is used to quantify the correlation between a sensing matrix $\Phi$ and a representation matrix $\Psi$. This incoherence ...
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1answer
1k views

Clustering algorithms that operate on sparse data matricies [closed]

I'm trying to compile a list of clustering algorithms that are: Implemented in R Operate on sparse data matrices (not (dis)similarity matrices), such as those created by the sparseMatrix function. ...
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167 views

How exactly is sparse PCA better than PCA?

I learnt about PCA a few lectures ago in class and by digging more about this fascinating concept, I got to know about sparse PCA. I wanted to ask, if I'm not wrong this is what sparse PCA is: In ...
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2answers
146 views

R or python implementation of sparse PCA for p>n

According to this paper, there are 2 algorithms to perform sparse PCA. One is better if $p>n$. I need to run SPCA on a $2000\times12000$ matrix so I am looking for an implementation of this ...
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1answer
66 views

Interpretation of lasso recovery results

When people say that lasso regression can under certain assumptions recover "the support", i.e. non-zero regression weights, what does this mean? This cannot mean causal recovery, because Pearl has ...
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0answers
13 views

Sparse variable selection algorithms that account for parameter changes

I am variable selecting for a time-series forecasting model that has parameters sampled from a high variance sampling distribution centred near zero and that undergo changes over time. Each predictor ...
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1answer
135 views

How Can I use some variables selected by LASSO?

I am very new about statistics. So, please understand if my question is somewhat awkward, and please give me related any advice. I have some data set. X = 500 x 100 (500 observations x 100 ...
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1answer
112 views

Limitations of Cohen's kappa for sparse data?

Are there any limitations for using Cohen's kappa with sparse data? I need inter-rater agreement between 2 raters for ~15 items, and the data in the contingency table is quite sparse (0 in some cells, ...
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1answer
40 views

Apply double non-negative constrain on Sparse PCA

In Non-negative Sparse PCA, we apply a non-negative constrain in the coordinate matrix. Here I'm up to apply non-negative constrains on both the basis matrix and the coordinate matrix. I'm wondering ...
4
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2answers
539 views

Why such a poor result from sparse PCA R package?

I'm preparing to use R to perform sparse analysis on my data. I tried to get started with an ad hoc example, but the reconstruction result turned out really poor. I'm wondering if I was making any ...
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2answers
192 views

What's the tractable data size for Sparse PCA or LASSO?

I'm up to perform certain kinds of sparse decomposition methods on my dataset. However, I'm not sure: what's the tractable data size for the Sparse Decomposition methods? The dataset is a ...
4
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1answer
682 views

Validate cluster analysis in R

I am trying to validate hierarchical cluster analysis result following a paper by Guy Brock, et al. clValid: An R Package for Cluster Validation (pdf). Do I have to use all these methods? What are the ...
3
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0answers
45 views

Sparsity regularization for eigenvectors

One way to think about finding the eigenvectors of a matrix $A$ is that they are the critical points of the functional $\vec x^\top A \vec x$ subject to $\|\vec x\|_2=1$. To regularize this problem, ...
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0answers
81 views

Methodology of modelling sparse events

Let us say that we have a process that generates sequences of the following form: ...
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3answers
524 views

Cosine similarity on sparse matrix

I'm trying to implement item based filtering, with a large feature space representing consumers who bought (1) or did not buy (0) a particular product. I have a long tail distribution, so the matrix ...
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0answers
70 views

Issues with solving large sparse linear equations

I have some issues solving sparse linear equations $Ax = b$ My matrix $A$ is sparse with dimension of 5 million by 5 million. Actually, it is a combination of two matrices. One is tridiagonal and the ...
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0answers
80 views

Sparse PCA adjusted variance explained

In the original Sparse PCA paper Sparse Principal Component Analysis ZOU, HASTIE, TIBSHIRANI they describe a way to compute the adjusted variance explained by computing QR decomposition of the Z ...
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2answers
362 views

Getting rid of a huge categorical factor in multiple regression

I have a large regression problem with a lot of cases, but relatively few independent variables. One of them is a categorical factor with thousands of levels. Robust regression runs forever. In some ...
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1answer
65 views

What's the convergence rate when solving L1 regularized optimization via coordinate descent with tiny step? [closed]

Wondering if there is an established result for the convergence rate when solving L1 regularized optimization via coordinate descent with tiny step? By "tiny step" I mean the step is always set to a ...
0
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0answers
64 views

Why do Bayesian methods achieve sparsity via normal likelihood and Gamma hyperprior?

In the view of $\ell_1$ regularization, a natural way to incorporate sparsity into a Bayesian model is to add a Laplacian prior in my opinion. But instead of this, a usual fashion to handle sparsity ...
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2answers
131 views

Sparse hyperspace clustering

I have a dataset of M elements where every item is represented by a feature vector of length N where N is very large and only a small subset of N is bigger then zero for every item. So I have a sparse ...
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3answers
2k views

Automatic outlier detection in R

Our model processes millions of multivariate observations; manual outlier detection is impractical. I am looking for a method of automatic outlier detection. I have been trying to use R package ...
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0answers
62 views

Confusion related to convexity and non convexity of a problem

I was reading this paper related to sparse PCA enter link description here I didn't understand how they said that the function was convex or non convex I wanted to know how in the screenshot ...
3
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1answer
297 views

Sparse representations for denoising problems

I have read in a huge number of papers that sparse models (sparse coding, dictionary learning, sparse matrix factorization, ...) are good solutions for image denoising problems. I know that ...
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2answers
445 views

Using sparse inverse covariance matrix in estimating least squares coefficients

I am reading the paper introducing the graphical lasso, which is a way to estimate a sparse inverse covariance matrix. http://www-stat.stanford.edu/~tibs/ftp/graph.pdf Finding a sparse inverse ...