Tagged Questions

The tag has no wiki summary.

learn more… | top users | synonyms

1
vote
0answers
27 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 ...
0
votes
0answers
20 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 ...
0
votes
0answers
18 views

Strategy to model, then predict / impute with very sparse variable?

Please excuse vague title. I am currently using an unsupervised SOM clustering approach to try to determine values for a variable that is mostly missing. I have ~8000 observations of 10 variables, the ...
3
votes
2answers
142 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 ...
-1
votes
1answer
39 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
votes
0answers
38 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 ...
1
vote
2answers
75 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 ...
0
votes
3answers
358 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 ...
1
vote
0answers
41 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
votes
1answer
132 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 ...
1
vote
2answers
238 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 ...
2
votes
2answers
153 views

Dimension reduction for sparse matrix for clustering

I'm looking for a Sparse matrix dimension reduction. I already used some feature selection methods like PCA but it doesn't give me good results. I want to apply mixture models for clustering my data. ...
2
votes
1answer
55 views

What is a sparse estimator?

Can anyone point me to a reference, either book or paper, where I can find the precise definition of sparse estimator? Thanks
1
vote
0answers
39 views

Enforcing sparsity on probability

I am trying to induce a probability distribution $Q$ by optimizing an objective function and am wondering how can one encourage sparsity for $Q$ while keeping the optimization convex. In particular, ...
1
vote
0answers
43 views

Sparse estimated data recommender system

Premise: For a product and a user, the system has to recommend him/her other non-users related with him/her that are most likely to be interested in that same product. Available data includes: ...
1
vote
0answers
126 views

Sparse matrix representation of a spline interpolation

I use spline interpolation within a statistical model, and the transpose of the operator turns up in the gradient of the log-likelihood. Let me set up some notation first. If $x_1 \ldots x_n$ are a ...
2
votes
1answer
133 views

Advice for a sparse high-dimensional regression strategy

I have a regression problem where I would like to predict values given several thousand sparse features. The general data set is an $n \times m$ matrix where each row contains a sample with a value I ...
7
votes
3answers
176 views

Sparsity-inducing regularization for stochastic matrices

It is well-known (e.g. in the field of compressive sensing) that the $L_1$ norm is "sparsity-inducing," in the sense that if we minimize the functional (for fixed matrix $A$ and vector $\vec{b}$) ...
1
vote
2answers
265 views

Hybrid (K-means + Hierarchical ) clustering

I have a huge dataset (50,000 2000-dimensional sparse feature vectors). I want to cluster them in to k (unknown)clusters. As hierarchical clustering is very expensive in terms of time complexity ...
7
votes
1answer
131 views

What are $\ell_p$ norms and how are they relevant to regularization?

I have been seeing a lot of papers on sparse representations lately, and most of them use the $\ell_p$ norm and do some minimization. My question is, what is the $\ell_p$ norm, and the $\ell_{p, q}$ ...
3
votes
0answers
62 views

Is there a (parametric) distribution over sparse symmetric, binary matrix-valued random variables?

The title says it all, really. I'm aware of the Wishart distribution for symmetric, nonnegative-definite matrix-valued random variables, and am looking for something along these lines, but for sparse ...
2
votes
0answers
45 views

Inducing sparsity in a Bayesian model

Can someone explain or point me to an introductory reference that deals with the notion of sparsity in Bayesian modeling? What does the idea of sparsity really mean? What does it mean to 'induce ...
14
votes
5answers
1k views

Euclidean distance is usually not good for sparse data?

I have seen somewhere that classical distances (like Euclidean distance) become weakly discriminant when we have multidimensional and sparse data. Why? Do you have an example of two sparse data ...
9
votes
2answers
553 views

Random forest implementation for sparse data

Is there an R random forest implementation that works well with very sparse data? I have thousands or millions of boolean input variables, but only hundreds or so will be TRUE for any given example. ...
1
vote
1answer
399 views

Using SparseM/Matrix Sparse Matrix in training SVM from e1071 returning different results from same data in standard matrix

Using sparse matrix objects in svm training in e1071 returns different results than running on the same data represented as standard matrix: ...
9
votes
1answer
129 views

Does a sparse training set adversely affect an SVM?

I'm trying to classify messages into different categories using an SVM. I've compiled a list of desirable words/symbols from the training set. For each vector, which represents a message, I set the ...