# 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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### How to create bins for sparse data?

There is a column that has 10 rows of continuous data , 8 of these 10 rows has 0 and rest has 5 and 10. How can I divide these into 5 bins.(as rest of my columns which are significant has 5 bins).
1answer
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### Feature selection for very sparse data

I have a dataset of dimension 3,000 x 24,000 (approximately) with 6 class label. But the data is very sparse. The number of non-zero values per sample ranges from 10-300 (approx) out of 24,000. The ...
2answers
170 views

### How can one generate a sequence of unique k-sparse matrices without rejection sampling for an arbitrary k efficiently?

I would like to efficiently generate $k$-sparse matrices, i.e., matrices that have only $k$ nonzero entries. The catch is that all such matrices must be different. Matrices are filled with 1 at the $k$...
0answers
38 views

### Periodicity in noisy data and the usefulness of differencing

Disclaimer: I don't have great stats knowledge. I'm doing some exploratory data analysis with the goal of detecting whether or not there is periodicity in the data set. I have a collection of photon ...
2answers
1k views

### Can I use PCA with mixed and sparse data types?

I am trying to reduce the dimensionality of a data set of about 100'000 rows and 1'000 columns, in order to cluster the individual observations with k-means. I tried PCA with rescaling (i.e., ...
2answers
558 views

0answers
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### t-SNE on a small sparse matrix

I performed t-SNE on a this small sparse matrix with 2 identical points: ...
1answer
2k views

### Compressed Sensing relationship to L1 Regularization

I understand that compressed sensing finds the sparsest solution to $$y = Ax$$ where $x \in \mathbb{R}^D$, $A \in \mathbb{R}^{k \times D}$, and $y \in \mathbb{R}^{k}$, $k << D$. In this way we ...
0answers
678 views

### Deep neural network: categorical cross entropy with l1-norm (sparsity)

I am using a deep neural network in order which consists of: 1i nput layer, 2 hidden (dense) layers, 2 dropout layers (right after each dense layer), 1 softmax classifier (output). The cost function ...
2answers
662 views

### Large-scale MAE regression in R

I have a large, sparse dgCMatrix matrix in R: ~200,000 rows ~150,000 columns ~1,000,000,000 non-zero entries R code to generate the matrix: ...
0answers
87 views

### Bayesian dictionary learning derivations

I am trying to do the derivations and implementation of dictionary learning/sparse coding in a Bayesian way. I am not sure if the derivations are correct, or maybe my approach is totally wrong. So ...
1answer
142 views

### Is correlation, as a metric in clustering, affected by sparsity? [closed]

I want to correlate one sample to a set of classes' centroids (i.e. for each class, the sample composed by the median value of each feature in the set of samples of the class) to understand which ...
0answers
166 views

### Sparse Representation, Sparse Learning, Sparse Coding, Group Sparse Coding and Group Sparse Learning?

I'm really confused with these terms for the relations and difference between them: Sparse Representation Sparse Learning Sparse Coding Group Sparse Coding Group Sparse Learning Sparse Dictionary ...
0answers
228 views

### Is there a statistical test for sparsity?

We are developing an algorithm that tries to reconstruct/impute missing data from sparse datasets. I would like to know how to asses or quantify the sparsity of a dataset? Is there an appropriate ...
2answers
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 ...
0answers
98 views

### Are factorization machines robust to outliers?

Factorization machines (FMs) seem great for modeling very sparse data. However, I have not come across much discussion regarding the impact of outliers. If FMs are robust, why is that so?
0answers
135 views

### Background required for understanding Robust PCA and Low-Rank Sparse Decomposition

My current knowledge is Linear Algebra, basics of statistics and Machine Learning (Andrew Ng's ML Coursera). I have a very good understanding of classic PCA and I know how to implement it in python ...
0answers
79 views

### e-SVM performance vs number of feature

I apply epsilon Support Vector Machine (e-SVM) to a regression problem via Weka. I have about 6000 features and 2000 samples. I order the feature respect to minimal-redundancy-maximal-relevance ...
1answer
233 views

### Any penalized ensemble classifiers in Scikit-learn that result in sparse solutions?

Is there an ensemble classifier that results in sparse solutions for the feature vector like Lasso Regression? With Logistic Regression, I can choose L1 penalization from the ...
0answers
842 views