Unanswered 'sparse' Questions

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815 views

Feature scaling for non-negative sparse data

Imagine you have many observations on which you want to run a classification algorithm. Each observation is characterized by a matrix of non-negative values. For all observations 90-98% of the values ...

asked May 24 '15 at 18:33

pir

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5

votes

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908 views

Compute Shannon entropy between every row of a large, sparse matrix

I have a sparse, binary matrix of user (rows) and items (columns). Each element of this matrix is either 0 or 1: ...

r distance-functions entropy similarities sparse

asked Mar 24 '14 at 20:07

Zach

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4

votes

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39 views

Probability distribution associated with nuclear norm?

The $\ell_1$ norm of model parameters is often added to loss functions because it induces sparsity in the solution of the overall cost function: $$ c(\theta) = \log L(x|\theta) + \lambda ||\theta||_1$$...

bayesian regularization matrix sparse multivariate-distribution

asked Dec 2 '20 at 0:38

John Madden

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348 views

How is explained variance in sparse PCA calculated?

Sparse PCA is a technique proposed by Zou et all in this paper. In usual PCA the obtained loadings are orthonormal, and the resulting scores are uncorrelated. However, in sparse PCA you give up these ...

r pca dimensionality-reduction sparse

asked Dec 3 '19 at 10:28

Álvaro Méndez Civieta

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50 views

using sparse models to accelerate training

I was going through a tutorial that highlights the importance of using sparse models to get "lightning fast models" i.e. to accelerate the training process. What are the issues in using a sparse ...

machine-learning sparse

asked Jan 10 '18 at 7:46

Upendra01

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4

votes

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165 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 ...

machine-learning terminology sparse

asked Nov 15 '16 at 12:44

a d

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votes

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818 views

Gibbs sampling for spike and slab priors

In Spike and slab variable selection (equation 4) there is a model setup of the form $\beta_k | \lambda_k, \tau_k \sim \text{Normal} (0, \lambda_k \tau_k^2)$ $\lambda_k | \nu_0, w \sim (1-w)\delta_{\...

bayesian feature-selection prior gibbs sparse

asked Oct 7 '16 at 13:20

kezz_smc

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3

votes

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46 views

What is the appropriate metric for determining distance / dissimilarity of sparse, high dimensional data in PCA space?

I'm working with scRNA-seq data (~96% sparse, high dimensional), and am trying to determine distances between the cells in PCA space - NOT for the specific purpose of clustering. The principal ...

pca matrix distance high-dimensional sparse

asked Sep 6 '20 at 0:33

jack_bioinf

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3

votes

1answer

199 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 ...

classification binary-data glmnet sparse

asked Jul 11 '19 at 7:05

Tikke

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3

votes

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30 views

Probability of sparse spectrum

Consider a vector $v$ such that $v \sim \mathrm{Unif}(\mathbb{S}^{d-1})$, the uniform distribution on the unit sphere in $d$ dimensions. Question: is there an upper bound on the probability that $v$ ...

sampling references sparse probability-inequalities fourier-transform

asked May 24 '19 at 21:01

VHarisop

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3

votes

0answers

295 views

How to conduct a principal component analysis on data set with large number of zeros

I have data for percentage cover of plant species in 500 sites. There are columns for 30 different species in the data set and I would like to drastically reduce this down to a manageable number of ...

pca dimensionality-reduction zero-inflation sparse correspondence-analysis

asked May 13 '19 at 13:22

Nebulloyd

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votes

1answer

405 views

Simulation of low rank and sparse matrix

I am having trouble simulating a matrix which is low rank and sparse (sparse along both rows and columns). One way to simulate a low-rank matrix is by generating a random matrix, then taking SVD and ...

simulation matrix sparse

asked Sep 26 '18 at 21:47

Dushyant Sahoo

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votes

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55 views

How to prove oracle properties in Fan and Li (2001) paper

I am studying Fan and Li's 2001 paper "Variable selection via nonconcave penalized Likelihood andits oracle properties" but I am having troubles understanding Theorem 1 proof (page 1359). I ...

mathematical-statistics estimation likelihood sparse regularization

asked Jul 11 '18 at 15:24

Álvaro Méndez Civieta

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3

votes

1answer

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 ...

machine-learning optimization unbalanced-classes supervised-learning sparse

asked Jul 6 '17 at 2:50

mlx

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votes

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96 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?

machine-learning outliers sparse

asked Oct 27 '16 at 1:29

user136404

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votes

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1k views

Efficient/feasible sparse matrix inversion in R

I am looking to perform a 2-stage least-squares estimation with sparse matrices in R, in the style of Bramoulle et al (J. Econometrics 2009). Specifically, let: G be a very sparse block-diagonal ...

r sparse matrix-inverse

asked Oct 3 '16 at 20:22

Ray

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3

votes

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3k views

Typical range of values for TFIDF

I am working on a text corpus. Each line contains between 10 and 50 words. There are around 25 000 words in the whole text and 1 000 000 lines. I turned this corpus into its tf-idf representation. I ...

text-mining sparse information-retrieval

asked Aug 7 '15 at 9:45

RUser4512

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3

votes

0answers

409 views

sparse covariance/correlation thresholding

In our project, we would like to do some optimization on sparse matrices. The idea is to scrape massive amounts of data, form a covariance/correlation matrix, and form a sparsity pattern basically by ...

correlation statistical-significance covariance regularization sparse

asked Mar 20 '15 at 23:29

Y. S.

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votes

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162 views

Random classification forests for extremely sparse response variables

I have a response variable that can be $A,B,C$. It is very sparse, meaning 99% of the sample is $B$ and the rest is approximately evenly divided between $A$ and $C$. How do I predict this variable in ...

random-forest cart sparse

asked Feb 6 '14 at 18:39

Jase

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votes

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102 views

Sparsity regularization for eigenvectors

One way to think about finding the eigenvectors of a matrix $A$ is that they are the critical points of the functional $\vec x^\top A \vec x$ subject to $\|\vec x\|_2=1$. To regularize this problem, ...

regularization sparse eigenvalues

asked Jul 4 '13 at 11:42

Justin Solomon

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3

votes

0answers

212 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 ...

distributions matrix binary-data sparse

asked Jul 6 '12 at 13:34

Chris

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2

votes

0answers

19 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....

machine-learning clustering natural-language k-means sparse

asked Feb 8 at 6:33

msamogh

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2

votes

1answer

19 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?

pca sparse

asked Dec 28 '20 at 20:34

BlueSky

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2

votes

1answer

91 views

Power simulation on glmer.nb gave strange results

I would like to ask for solution or advice on strange result that glmer.nb from lme4 generated when simulating using simR package. I’m working on longitudinal gut microbiome abundance data (23 ...

simulation glmm negative-binomial-distribution statistical-power sparse

asked Apr 16 '20 at 13:46

Jessica Chen

211 bronze badge

2

votes

0answers

34 views

How to interpret the learning curve of a LinearRegression for sparse data?

I have a dataset of shape ca.(4800, 350). Both the dataset X as well as the response y is very sparse (ca. 3500 samples with y=0). I wanted to take a look at the learning curve to estimate the bias-...

regression linear sparse

asked Sep 12 '19 at 9:10

the_man_in_black

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2

votes

0answers

173 views

Does anyone know the rank of the Netflix Prize dataset?

I'm looking into the Netflix Prize at the moment. We model the dataset as an $n \times m$ matrix, where $n$ is the number of users and $m$ is the number of movies. Does anyone know the rank of the ...

matrix linear-algebra recommender-system sparse

asked Mar 19 '19 at 17:23

stats_man97531

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2

votes

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34 views

Is low rank finite-iteration manifold identification possible?

In sparse optimization, I am trying to solve the problem $$ \min_{x\in \mathbb R^{n}} \quad f(x) + \|x\|_1 $$ and at optimality, $x^*$ may be sparse. If I define the sparse manifold as $\mathcal M = ...

svd sparse manifold-learning reduced-rank-regression

asked Nov 25 '18 at 23:39

Y. S.

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2

votes

0answers

240 views

GAN and NN for sparse data

I have a set of images which represent some correlated sparse data $x_1,\ldots ,x_n$. there are a number of specific pixels in the images which might hold value or not (with some probability), while ...

neural-networks sparse gan

asked Oct 7 '18 at 12:15

havakok

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2

votes

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357 views

How to transform my sparse count data into normal distribution?

I am running glm on beetle counts data. My predictors are environmental variables and my response variable is the number of beetles. I ran three glms: The response variable $Y_1$ is the total number ...

count-data sparse

asked May 29 '18 at 6:39

Noelline Tsafack

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2

votes

0answers

1k views

how to handle the sparse data in machine learning for novelty detection using SVM in r?

I have a data frame wherein 89000 records 60% of the values are missing I am replacing the missing values by ...

r machine-learning svm dataset sparse

asked Dec 10 '17 at 3:13

vinaykva

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2

votes

0answers

79 views

How can lasso CCA be solved using LARS?

According to paper By Sun, Ji an Ye; A Least Squares Formulation for Canonical Correlation Analysis http://www.machinelearning.org/archive/icml2008/papers/270.pdf CCA can be reformulated as a least ...

lasso sparse canonical-correlation lars

asked Jun 30 '17 at 17:48

rep_ho

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2

votes

0answers

305 views

t-SNE on a small sparse matrix

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

dimensionality-reduction sparse tsne

asked Jan 13 '17 at 4:30

Nitro

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2

votes

0answers

86 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 ...

bayesian optimization sparse bayesian-optimization

asked Dec 1 '16 at 7:04

groove

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2

votes

2answers

468 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 ...

optimization lasso sparse

asked Nov 1 '16 at 17:32

Neophyte

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2

votes

0answers

80 views

Are there kernel-based one-class sparse kernel-based outlier detection methods, e.g. one-class Relevance Vector Machine?

I have a commercial outlier detection problem in moderate dimension (8-25). We have a limited number of true positive tags and can roughly evaluate performance of various methods. So far, the 1-...

svm outliers kernel-trick sparse one-class

asked Apr 23 '16 at 22:29

drchaos

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2

votes

0answers

181 views

Clustering algorithm advice for extracting key features in sparse data

I have the following dataset: consider a dataset $X$ of $1400 \times 600$. The rows represent households at time $1 \leq t \leq 14$. So I have $100$ households. The columns represent the programs that ...

r clustering sparse

asked Aug 22 '15 at 20:13

Snowflake

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2

votes

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583 views

Miss Forest & Iterative PCA : How to handle very sparse matrix imputation?

I am currently benchmarking matrix completion methods (k-NN, RandomForest and iterative PCA) on multivariate normal data in which I introduce a certain proportion of NA (5 to 95%). My performance ...

pca random-forest simulation missing-data sparse

asked Aug 21 '15 at 16:52

Anas El Khaloui

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2

votes

0answers

588 views

How to go from sparse matrix to linear regression model (using SVD)?

I am trying to replicate the Kosinski, Stillwell, & Graepel (2013) study about predicting private traits and attributes from Facebook like data for study purposes. First I have admit, however, ...

r regression prediction svd sparse

asked Jul 9 '15 at 19:18

Boasbam

211 bronze badge

2

votes

0answers

107 views

Instances of sparse covariance matrices

I am trying to find large datasets with inherently sparse covariance matrices, to be tested with our algorithm. Basically, we will take the sample covariance matrix and enforce some structured ...

covariance large-data sparse

asked Mar 25 '15 at 21:28

Y. S.

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votes

0answers

770 views

Reduction of sparse features for machine learning

I'm trying to use a 1D histogram as a feature for machine learning. A histogram instance can be very sparse and the range of its bins is theoretically unbounded. Moreover it is expected that non-zero ...

dimensionality-reduction feature-engineering sparse

asked Sep 29 '14 at 9:07

Bohumir Zamecnik

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2

votes

0answers

835 views

The name data sparsity in different applications

I am recently surveying the techniques or algorithms which handle the data sparsity problems in various fields. And I find quite similar name "data sparsity" or "sparse data" is used including the ...

recommender-system sparse statistical-learning

asked Aug 26 '14 at 16:43

bliuab

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2

votes

0answers

338 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 ...

pca optimization sparse

asked Jan 9 '13 at 4:41

user34790

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2

votes

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589 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 ...

r interpolation splines sparse

asked Oct 29 '12 at 10:40

dahtah

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vote

0answers

17 views

Enforcing sparsity constraints that make use of spatial contiguity

I have a deep learning network that outputs grayscale image reconstructions. In addition to good reconstruction performance (measured through mean squared error or some other measure like psnr), I ...

neural-networks lasso regularization image-processing sparse

asked Sep 17 '20 at 20:59

Jane Sully

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vote

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22 views

Is K-medoids / partitioning around medoids (PAM) appropriate for clustering data with many zero values?

I need to cluster a matrix which contains zero values. I am clustering three separate sets of 24 values. The first two are non-zero and represent hourly ambient temperature (in K) and electrical ...

clustering data-mining sparse k-medoids

asked Apr 28 '20 at 15:29

Martin

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vote

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36 views

For ridge regression, show if $K$ columns of $X$ are identical then we must have same corresponding parameters

Show if $K$ columns of $X$, $({X_{j1}, X_{j2}...X_{jk}}) $are identical then we must have $\hat\beta_{j1},\hat\beta_{j2},...\hat\beta_{jk} $ are same in the ridge regression: $$\hat\beta = \underset{...

lasso ridge-regression sparse

asked Mar 6 '20 at 22:16

FantasticAI

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vote

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66 views

Sparse PCA for $p >> n$ solution with Elastic Net

I was reading about the sparse principal component approach by Zou, Hastie and Tibshirani but I do not quite understand how they handle the $p \gg n$ case in their paper. To derive the sparse axis, ...

pca lasso high-dimensional sparse elastic-net

asked Feb 14 '20 at 21:23

tobote

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vote

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37 views

Is it useful to use sparse regression (e.g. Lasso) when the number of observations is significantly larger than the number of covariates?

I'm learning about penalized/sparse regression and I noticed that the examples used for penalized/sparse regression, e.g. Lasso, are usually cases where the number of observations is significantly ...

regression lasso sparse regularization

asked May 16 '19 at 3:43

Ijies

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vote

1answer

101 views

finding sparse regions in time series data

I have several hundred years of church baptisms that will be searched by people wanting to find the baptisms of their ancestors. I want to call attention to periods in the records in which the number ...

time-series sparse

asked Nov 18 '18 at 20:29

kevinafra

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vote

0answers

175 views

Normalizing sparse matrix by mean, should the mean be calculated excluding zero?

I have very sparse matrix (70% sparsity) which I want to normalize by mean. I tried using mean both include and exclude zero. The histogram between count (y-axis) and value (x-axis) shows The value ...

distributions mean normalization sparse matrix-decomposition

asked Oct 23 '18 at 1:44

Jan

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