# Questions tagged [sparse]

A sparse matrix is a matrix where many of the elements are zeros. The tag can also be used for sparsity in other contexts, such as regression models with sparsity, or the "bet on sparsity"-principle.

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### What is the method in dictionary learning that does not have an overcomplete dictionary?

What is the method in dictionary learning that does not have an overcomplete dictionary? And what is the difference in minimization between these two methods (one using overcomplete dictionary and ...
264 views

### Combining many sparse binary variables

Based on kjetil b halvorsen suggestion, I rephrased my problem: My problem is analogous to the following: i am supposed to predict if a high school student will go to university (Yes/No). I have ...
35 views

### LARS package in R with specific lambda value

I am trying to use the LARS package in R to obtain a Lasso estimate of the sparse coefficient vector, say $\hat{\beta}_{\text{sparse}}$, as opposed to a coefficient path. In other words, I am not ...
500 views

### Tuning parameter in the LASSO/group LASSO

I have a problem regarding the tuning parameter $\lambda$ in the LASSO or group LASSO. Suppose I want to find a matrix $\mathbf{A} = [\mathbf{a}_1,...,\mathbf{a}_n]\in\cal{C}^{m\times n}$ that ...
35 views

### Score Function for Sparse Mean Gaussian?

The Sparse Mean Gaussian model can be described by $X \sim \mathcal{N}(\theta, \sigma^2 I_d)$ where $\vert\vert\theta\vert\vert_{0} = s$ where $d$ is the dimension of the random variable, and $s$ ...
236 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 ...
2k views

### Optimization algorithms for sparse data

For couple of weeks now I've been dealing with a classification problem involving a sparse dataset. To be more specific, for each input $x^{(i)}$, knowing that I have 1000 features, I've only 5 to 10 ...
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### Clustering text embeddings: TF-IDF + BERT Sentence Embeddings

I am trying to cluster a few thousand forum posts that are similar in content to Stackoverfow. So far, I have tried two main approaches to represent the posts: TF-IDF Sentence embedding based on BERT....
10 views

### Difference between Structural Topic Modeling(STM) and SAGE (Sparse Additive Generative Model)?

I have read that STM combines 3 models of: (1) correlated topic model (CTM) (2) Dirichlet-Multinomial Regression (DMR) topic model (3) Sparse Additive Generative Model (SAGE) Is it correct to just ...
12 views

### Machine Learning algorithms for sparse database

I have a very sparse database with more than 200000 rows (instances) and 500 columns that lead to almost 100 million entries. However, only 205000 of the data are non zero, that is almost 0.2% of the ...
7 views

### Comparing sparse vectors

I am looking for a metric for comparing gene count tables. These are long columns of data (a few millions genes by a few dozen samples), with all non-negative entries, about 90% of which are zeros. ...
119 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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### Why atoms in the dictionary of Dictionary Learning method are not required to be orthogonal?

According to Sparse Dictionary Learning (wiki), Sparse dictionary learning is a representation learning method which aims at finding a sparse representation of the input data (also known as sparse ...