# 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.

289 questions
Filter by
Sorted by
Tagged with
11 views

### Loss of captured variance in sPCA

Good morning to all I am trying to run the Sparse Principal Component on a synthetic dataset I created. I noticed that the captured variance does not reach 100% (cumulative proportion), but on the ...
14 views

### Recommendation for books about statistical data analysis with sparse data ( a lot of zeros)

Evident from the title, i am looking for books about the general statistical data analysis techniques such as hypothesis testing, etc but for large and sparse dataset.
7 views

### How to choose tuning parameters for SparsePCA

Good morning to everyone. I'm studying the paper "Sparse Principal Component Analysis Hui ZOU, Trevor HASTIE, and Robert TIBSHIRANI" (Link: https://hastie.su.domains/Papers/spc_jcgs.pdf) At ...
9 views

### Obtain principal components from the loading vectors

Good morning, everyone. I am trying to use the "SparsePCA()" function of Matlab whose documentation link is below available. https://www.ml.uni-saarland.de/code/sparsePCA/sparsePCA.html This ...
62 views

### Creating a random sparse precision matrix?

In my current project, I want to create a random sparse precision matrix $\boldsymbol{P}=\boldsymbol{\Sigma}^{-1}$ (the inverse of a covariance matrix $\boldsymbol{\Sigma}$). My current procedure ...
• 473
100 views

• 353
32 views

### Bayes prior in MAP estimation corresponding to $\ell^0$ penalization

I gather that in the context of penalized least squares, we can interpret a penalty term as corresponding to a prior $\pi(\beta)\propto \exp\{-\text{pen}\}.$ Is this also true for $\ell^0$ ...
• 353
172 views

### Bayesian priors associated with regularization penalties

I gather that adding a penalty term to (linear) least squares minimization typically corresponds with choosing some prior for Bayes estimation in the normal linear regression model. A couple questions ...
• 353
27 views

### LASSO with $L_p$ norms for $1 < p < 2$?

For the sparse linear regression problem, minimizing the LASSO objective $\| X \beta - Y \|_2^2 + \lambda \| \beta \|_1$ is known to recover the optimal data generating parameter $\beta^*$ with the ...
• 101
124 views

### regarding the loss function for the sparse autoencoder

I am implementing a sparse autoencoder using dictionary learning framework, i.e. instead of using a neural network in Keras, Pytorch or Tensorflow, I am using only matrices to represent the different ...
• 1,596
20 views

### Doubts about generating a synthetic dataset according to a paper

I'm trying to replicate the experiment reported in section 3.3 of this paper (https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2930825/) but I'm struggling to understand how the synthetic dataset is ...
8 views

• 2,301
79 views

### Are Spiking Neural Networks The Next Big Thing? [closed]

Intel recently announced their Loihi chip as part of their "Neuromorphic Computing" research, which is optimized for spiking neural networks (SNNs). I found an example of a problem in which ...
• 423