no code implementations • 16 Oct 2023 • Xin Bing, Dian Jin, Yuqian Zhang
Vintage factor analysis is one important type of factor analysis that aims to first find a low-dimensional representation of the original data, and then to seek a rotation such that the rotated low-dimensional representation is scientifically meaningful.
no code implementations • 25 Oct 2022 • Xin Bing, Marten Wegkamp
A generalized least squares estimator is used to estimate the direction of the optimal separating hyperplane.
no code implementations • 23 Oct 2022 • Xin Bing, Marten Wegkamp
In high-dimensional classification problems, a commonly used approach is to first project the high-dimensional features into a lower dimensional space, and base the classification on the resulting lower dimensional projections.
no code implementations • 26 Jun 2022 • Xin Bing, Florentina Bunea, Jonathan Niles-Weed
Our results establish this metric to be a canonical choice.
no code implementations • 12 Jul 2021 • Xin Bing, Florentina Bunea, Seth Strimas-Mackey, Marten Wegkamp
When $A$ is unknown, we estimate $T$ by optimizing the likelihood function corresponding to a plug in, generic, estimator $\hat{A}$ of $A$.
1 code implementation • NeurIPS 2021 • Dian Jin, Xin Bing, Yuqian Zhang
In this paper, we study the problem of seeking a unique decomposition of a low rank matrix $Y\in \mathbb{R}^{p\times n}$ that admits a sparse representation.
no code implementations • 20 Jul 2020 • Xin Bing, Florentina Bunea, Seth Strimas-Mackey, Marten Wegkamp
Our primary contribution is in establishing finite sample risk bounds for prediction with the ubiquitous Principal Component Regression (PCR) method, under the factor regression model, with the number of principal components adaptively selected from the data -- a form of theoretical guarantee that is surprisingly lacking from the PCR literature.
no code implementations • 22 Jan 2020 • Xin Bing, Florentina Bunea, Marten Wegkamp
We derive a finite sample upper bound for our estimator, and show that it matches the minimax lower bound in many scenarios.
1 code implementation • 17 May 2018 • Xin Bing, Florentina Bunea, Marten Wegkamp
We propose a new method of estimation in topic models, that is not a variation on the existing simplex finding algorithms, and that estimates the number of topics K from the observed data.
no code implementations • 23 Apr 2017 • Xin Bing, Florentina Bunea, Yang Ning, Marten Wegkamp
This work introduces a novel estimation method, called LOVE, of the entries and structure of a loading matrix A in a sparse latent factor model X = AZ + E, for an observable random vector X in Rp, with correlated unobservable factors Z \in RK, with K unknown, and independent noise E. Each row of A is scaled and sparse.