scholarly journals A maximum uncertainty LDA-based approach for limited sample size problems — with application to face recognition

2006 ◽  
Vol 12 (2) ◽  
pp. 7-18 ◽  
Author(s):  
Carlos Eduardo Thomaz ◽  
Edson Caoru Kitani ◽  
Duncan Fyfe Gillies
Keyword(s):  
Face Recognition ◽  
Sample Size ◽  
Limited Sample ◽  
Limited Sample Size

scholarly journals A maximum uncertainty LDA-based approach for limited sample size problems - with application to face recognition

2006 ◽  
Vol 12 (2) ◽  
pp. 7-18
Author(s):  
Carlos Eduardo Thomaz ◽  
Edson Caoru Kitani ◽  
Duncan Fyfe Gillies
Keyword(s):  
Face Recognition ◽  
Sample Size ◽  
Limited Sample ◽  
Limited Sample Size

scHinter: imputing dropout events for single-cell RNA-seq data with limited sample size

2019 ◽  
Author(s):  
Pengchao Ye ◽  
Wenbin Ye ◽  
Congting Ye ◽  
Shuchao Li ◽  
Lishan Ye ◽  
...  
Keyword(s):  
Gene Expression ◽  
Sample Size ◽  
Single Cell ◽  
Wide Spectrum ◽  
Dropout Rate ◽  
Supplementary Information ◽  
Sample Sizes ◽  
Limited Sample ◽  
Limited Sample Size ◽  
The Impact

Abstract Motivation Single-cell RNA-sequencing (scRNA-seq) is fast and becoming a powerful technique for studying dynamic gene regulation at unprecedented resolution. However, scRNA-seq data suffer from problems of extremely high dropout rate and cell-to-cell variability, demanding new methods to recover gene expression loss. Despite the availability of various dropout imputation approaches for scRNA-seq, most studies focus on data with a medium or large number of cells, while few studies have explicitly investigated the differential performance across different sample sizes or the applicability of the approach on small or imbalanced data. It is imperative to develop new imputation approaches with higher generalizability for data with various sample sizes. Results We proposed a method called scHinter for imputing dropout events for scRNA-seq with special emphasis on data with limited sample size. scHinter incorporates a voting-based ensemble distance and leverages the synthetic minority oversampling technique for random interpolation. A hierarchical framework is also embedded in scHinter to increase the reliability of the imputation for small samples. We demonstrated the ability of scHinter to recover gene expression measurements across a wide spectrum of scRNA-seq datasets with varied sample sizes. We comprehensively examined the impact of sample size and cluster number on imputation. Comprehensive evaluation of scHinter across diverse scRNA-seq datasets with imbalanced or limited sample size showed that scHinter achieved higher and more robust performance than competing approaches, including MAGIC, scImpute, SAVER and netSmooth. Availability and implementation Freely available for download at https://github.com/BMILAB/scHinter. Supplementary information Supplementary data are available at Bioinformatics online.

The predictive performances of random forest models with limited sample size and different species traits

2020 ◽  
Vol 227 ◽  
pp. 105534 ◽  
Author(s):  
Jing Luan ◽  
Chongliang Zhang ◽  
Binduo Xu ◽  
Ying Xue ◽  
Yiping Ren
Keyword(s):  
Random Forest ◽  
Sample Size ◽  
Species Traits ◽  
Limited Sample ◽  
Limited Sample Size ◽  
Forest Models ◽  
Random Forest Models

scholarly journals Prediction in $\mathcal{M}$ -complete Problems with Limited Sample Size

2013 ◽  
Vol 8 (3) ◽  
pp. 647-690 ◽  
Author(s):  
Jennifer Lynn Clarke ◽  
Bertrand Clarke ◽  
Chi-Wai Yu
Keyword(s):  
Sample Size ◽  
Limited Sample ◽  
Limited Sample Size ◽  
Complete Problems

scholarly journals Synthetic observations from deep generative models and binary omics data with limited sample size

2020 ◽  
Author(s):  
Jens Nußberger ◽  
Frederic Boesel ◽  
Stefan Lenz ◽  
Harald Binder ◽  
Moritz Hess
Keyword(s):  
Sample Size ◽  
Binary Data ◽  
Generative Models ◽  
Small Sample ◽  
Generative Adversarial Networks ◽  
Omics Data ◽  
Sample Sizes ◽  
Odds Ratios ◽  
Limited Sample ◽  
Limited Sample Size

AbstractDeep generative models can be trained to represent the joint distribution of data, such as measurements of single nucleotide polymorphisms (SNPs) from several individuals. Subsequently, synthetic observations are obtained by drawing from this distribution. This has been shown to be useful for several tasks, such as removal of noise, imputation, for better understanding underlying patterns, or even exchanging data under privacy constraints. Yet, it is still unclear how well these approaches work with limited sample size. We investigate such settings specifically for binary data, e.g., as relevant when considering SNP measurements, and evaluate three frequently employed generative modeling approaches, variational autoencoders (VAEs), deep Boltzmann machines (DBMs) and generative adversarial networks (GANs). This includes conditional approaches, such as when considering gene expression conditional on SNPs. Recovery of pair-wise odds ratios is considered as a primary performance criterion. For simulated as well as real SNP data, we observe that DBMs generally can recover structure for up to 100 variables with as little as 500 observations, with a tendency of over-estimating odds ratios when not carefully tuned. VAEs generally get the direction and relative strength of pairwise relations right, yet with considerable under-estimation of odds ratios. GANs provide stable results only with larger sample sizes and strong pair-wise relations in the data. Taken together, DBMs and VAEs (in contrast to GANs) appear to be well suited for binary omics data, even at rather small sample sizes. This opens the way for many potential applications where synthetic observations from omics data might be useful.

scholarly journals Techniques to Produce and Evaluate Realistic Multivariate Synthetic Data

2021 ◽  
Author(s):  
John Heine ◽  
Erin E.E. Fowler ◽  
Anders Berglund ◽  
Michael J. Schell ◽  
Steven A Eschrich
Keyword(s):  
Sample Size ◽  
Random Number Generator ◽  
Synthetic Data ◽  
Principal Component ◽  
Limited Sample ◽  
Limited Sample Size ◽  
Input Sample ◽  
Size Condition ◽  
Input Variables ◽  
Pca Transform

Background: Proper data modeling in biomedical research requires sufficient data for exploration and reproducibility purposes. A limited sample size can inhibit objective performance evaluation. Objective: We are developing a synthetic population (SP) generation technique to address the limited sample size condition. We show how to estimate a multivariate empirical probability density function (pdf) by converting the task to multiple one-dimensional (1D) pdf estimations. Methods: Kernel density estimation (KDE) in 1D was used to construct univariate maps that converted the input variables (X) to normally distributed variables (Y). Principal component analysis (PCA) was used to transform the variables in Y to the uncoupled representation (T), where the univariate pdfs were assumed normal with specified variances. A standard random number generator was used to create synthetic variables with specified variances in T. Applying the inverse PCA transform to the synthetic variables in T produced the SP in Y. Applying the inverse maps produced the respective SP in X. Multiple tests were developed to compare univariate and multivariate pdfs and covariance matrices between the input (sample) and synthetic samples. Three datasets were investigated (n = 667) each with 10 input variables. Results: For all three datasets, both the univariate (in X, Y, and T) and multivariate (in X, Y, and T) tests showed that the univariate and multivariate pdfs from synthetic samples were statistically similar to their pdfs from the respective samples. Application of several tests for multivariate normality indicated that the SPs in Y were approximately normal. Covariance matrix comparisons (in X and Y) also indicated the same similarity. Conclusions: The work demonstrates how to generate multivariate synthetic data that matches the real input data by converting the input into multiple 1D problems. The work also shows that it is possible to convert a multivariate input pdf to a form that approximates a multivariate normal, although the technique is not dependent upon this finding. Further studies are required to evaluate the generalizability of the approach.

scholarly journals Machine learning algorithm validation with a limited sample size

PLoS ONE ◽  
2019 ◽  
Vol 14 (11) ◽  
pp. e0224365 ◽  
Author(s):  
Andrius Vabalas ◽  
Emma Gowen ◽  
Ellen Poliakoff ◽  
Alexander J. Casson
Keyword(s):  
Machine Learning ◽  
Sample Size ◽  
Learning Algorithm ◽  
Machine Learning Algorithm ◽  
Limited Sample ◽  
Limited Sample Size ◽  
Algorithm Validation
Sign in / Sign up

Export Citation Format

Share Document