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.

277 questions
Filter by
Sorted by
Tagged with
20 views

I'm using Dr Frank Harrell's code in RMS 2nd edition. He goes into sparse PCA. Does anyone know how to code a regression model after getting the sparse component grid? ...
• 21
25 views

Fitting Sparsed Constrained regression with non-negative coefficients adding to 1

I see a similar problem in How do I fit a constrained regression in R so that coefficients total = 1? Specifically, my model is $Y_i= \pi_1 X_1+\pi_2 X_2 +...+ \pi_K X_K +\epsilon_i$ with $\pi_k \ge 0$...
19 views

• 231
38 views

Variable selection with sparse data

I have a dataset with 141 observations and 8 corresponding variables and I mean to apply a GLM to this dataset. However, a lot of observations lack either one or multiple variable values. So if I want ...
• 11
10 views

What is the degree of cell sparsity that Poisson model can tolerate?

For models with no interaction terms, my understanding is that all the marginal cells need to have non-zero counts, in order to have finite estimates. If this minimum requirement is satisfied, will a ...
• 475
1 vote
22 views

How to compare row entries in a sparse table with lots of missing values?

I have a dataset with ~1000 laptops and performance results across ~100 different benchmarks. Using the benchmark results, I want to give each laptop a single composite performance score, and rank the ...
80 views

Autoencoder doesn't learn 'sparse' input images

I am trying to train an autoencoder with PyTorch on 2D images containing 2D Gaussian densities such as this: The images are of size 100x100 (I feed them into the autoencoder as 1x10000 tensors). The ...
1 vote
31 views

What is the difference between network sparsification and model pruning

What is the difference between network sparsification and model pruning? I watched USENIX ATC '21 - Octo: INT8 Training with Loss-aware Compensation and Backward Quantization for Tiny (at 01:29sec) ...
• 189
38 views

What are the references for different active set selection methods for sparse Gaussian processes?

I am comparing the different sparse Gaussian process approaches within the fitrgp function in Matlab, but I am struggling to find references for the different choices within the function for the ...
• 21
24 views

Fit a histogram of sparse data with a monotonically decreasing function

I have a business problem where essentially we have counts across a certain metric which at times suffer from low observation counts! So essentially when you look across the bins you'll have adequate ...
• 21
43 views

Compatibility condition in LASSO

I am reading Statistics for High-Dimensional Data (Bühlmann and van de Geer). Chapter 6 discusses obtaining the oracle inequality in LASSO under the compatibility condition, a technical assumption ...
• 353
39 views

• 51
64 views

binary one dimensional k-svd

I have a problem that is somehow between k-means and k-svd: I would like to find a vector $D=[d_1, d_2, d_3..., d_k]$, so that the elements of another vector $Y=[y_1,y_2,y_3..., y_n]$ with $k \ll n$ ...
• 1
65 views

If there is a sparse solution then is the smallest l1 norm solution at least as sparse?

Consider the linear equation $Ax=b$ where $A$ is a matrix and $b$ and $x$ are vectors. Suppose there exists a vector $x_S$ that solves this equation ($Ax_S=b$) and $x_S$ has $k$ entries of value $0$ (...
39 views

Multiple regression with sparse X

I would like to run the multiple regression: $$y = X \beta + \epsilon \,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,(1)$$ with: $n \sim 2 \times 10^6$ $p \sim 10^4$ $X$ sparse 70% of rows have 1 non-...
49 views

In a sparse reward problem, is it possible to remove reward shaping once the RL agent trains long enough to consistently reach the final reward?

I'm new to machine learning, and primarily looking at it from the perspective of it's applications to control theory. In the application found in this paper, a RL agent attempts to land a spacecraft ...
• 23
20 views

Issues in having high-dimensional and sparse data

I was wondering about the issues one would encounter in a Machine Learning algorithm having data represented by high-dimensional vectors that are also sparse. In particular, I know that having many ...
• 427
19 views

Recommender System without Ratings but Duration instead

I'm currently working on a recommender system without ratings variable. I only have the watch duration for streamers and I should be able to recommend a list of streamers with importance on its order. ...
74 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 ...
• 1,030
1 vote
58 views

outlier detection for sparse data in categorical variable

I have a big dataset with a column "clientid" and a categorical column "choice". I want to find out what are the clients that have strange combinations of choices (less frequent ...
39 views

Calculate Data Sparsity - There are no 'zeros'

I am being asked to provide the percentage of data sparsity in my dataset. The challenge I am running into is that there isn't traditional sparsity in my data, no 'zeros', 'NAs', '-' etc.. I am ...
177 views

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....
• 41
278 views

Scaling a sparse matrix

I want to apply sparse PCA to a sparse matrix. I was wondering if scaling to mean 0 and unit variance would be appropriate given that my input is sparse?
• 21
199 views

• 427