Austin Talbot,Corey J Keller,Cristina Trevino et al.
Austin Talbot et al.
Interest in detecting networks responsible for phenotypes spans multiple scientific disciplines, including proteomics and neuroscience. Latent variable models, such as Principal Component Analysis (PCA), are a natural choice for such networ...
Meimei Liu,David B Dunson
Meimei Liu
When there is a distributional shift between data used to train a predictive algorithm and current data, performance can suffer. This is known as the domain adaptation problem. Bootstrap aggregating, or bagging, is a popular method for impr...
Woo Min Kim,Sutanoy Dasgupta,Pavan Turaga et al.
Woo Min Kim et al.
Signal estimation from noisy data is a fundamental problem in signal processing and data analysis. Existing literature offers various estimators based on different model choices and estimation criteria. This paper uses an innovative framewo...
An efficient solution to Hidden Markov Models on trees with coupled branches [0.03%]
具有耦合分支的树上隐马尔可夫模型的有效解法
Farzan Vafa,Sahand Hormoz
Farzan Vafa
Hidden Markov Models (HMMs) are powerful tools for modeling sequential data, where the underlying states evolve in a stochastic manner and are only indirectly observable. Traditional HMM approaches are well-established for linear sequences,...
Ben Gabrielson,Hanlu Yang,Trung Vu et al.
Ben Gabrielson et al.
Joint blind source separation (JBSS) involves the factorization of multiple matrices, i.e. "datasets", into "sources" that are statistically dependent across datasets and independent within datasets. Despite this usefulness for analyzing mu...
Alexander Tong,Frederik Wenkel,Dhananjay Bhaskar et al.
Alexander Tong et al.
We propose a new graph neural network (GNN) module, based on relaxations of recently proposed geometric scattering transforms, which consist of a cascade of graph wavelet filters. Our learnable geometric scattering (LEGS) module enables ada...
Puoya Tabaghi,Michael Khanzadeh,Yusu Wang et al.
Puoya Tabaghi et al.
Principal Component Analysis (PCA) is a workhorse of modern data science. While PCA assumes the data conforms to Euclidean geometry, for specific data types, such as hierarchical and cyclic data structures, other spaces are more appropriate...
An optimal pairwise merge algorithm improves the quality and consistency of nonnegative matrix factorization [0.03%]
一种优化的配对融合算法提高了非负矩阵分解的质量和一致性
Youdong Guo,Timothy E Holy
Youdong Guo
Non-negative matrix factorization (NMF) is widely used for dimensionality reduction of large datasets and is an important feature extraction technique for source separation. However, NMF algorithms may converge to poor local minima, or to o...
A tensor based varying-coefficient model for multi-modal neuroimaging data analysis [0.03%]
一种基于张量的可变系数模型在多模态脑影像数据分析中的应用
Pratim Guha Niyogi,Martin A Lindquist,Tapabrata Maiti
Pratim Guha Niyogi
All neuroimaging modalities have their own strengths and limitations. A current trend is toward interdisciplinary approaches that use multiple imaging methods to overcome limitations of each method in isolation. At the same time neuroimagin...
Multitaper Analysis of Semi-Stationary Spectra from Multivariate Neuronal Spiking Observations [0.03%]
多变量神经脉冲观测半定态谱的多重突锥分析
Anuththara Rupasinghe,Behtash Babadi
Anuththara Rupasinghe
Extracting the spectral representations of neural processes that underlie spiking activity is key to understanding how brain rhythms mediate cognitive functions. While spectral estimation of continuous time-series is well studied, inferring...