Zobrazeno 1 - 10
of 161
pro vyhledávání: '"Wegkamp, Marten"'
Mixed multinomial logits are discrete mixtures introduced several decades ago to model the probability of choosing an attribute from $p$ possible candidates, in heterogeneous populations. The model has recently attracted attention in the AI literatur
Externí odkaz:
http://arxiv.org/abs/2409.09903
Linear Discriminant Analysis (LDA) is an important classification approach. Its simple linear form makes it easy to interpret and it is capable to handle multi-class responses. It is closely related to other classical multivariate statistical techniq
Externí odkaz:
http://arxiv.org/abs/2402.14260
Autor:
Bing, Xin, Wegkamp, Marten
This paper considers binary classification of high-dimensional features under a postulated model with a low-dimensional latent Gaussian mixture structure and non-vanishing noise. A generalized least squares estimator is used to estimate the direction
Externí odkaz:
http://arxiv.org/abs/2210.14347
Autor:
Bing, Xin, Wegkamp, Marten
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. In this paper, we form
Externí odkaz:
http://arxiv.org/abs/2210.12862
This paper studies the estimation of high-dimensional, discrete, possibly sparse, mixture models in topic models. The data consists of observed multinomial counts of $p$ words across $n$ independent documents. In topic models, the $p\times n$ expecte
Externí odkaz:
http://arxiv.org/abs/2107.05766
Detecting approximate replicate components of a high-dimensional random vector with latent structure
High-dimensional feature vectors are likely to contain sets of measurements that are approximate replicates of one another. In complex applications, or automated data collection, these feature sets are not known a priori, and need to be determined. T
Externí odkaz:
http://arxiv.org/abs/2010.02288
This work is devoted to the finite sample prediction risk analysis of a class of linear predictors of a response $Y\in \mathbb{R}$ from a high-dimensional random vector $X\in \mathbb{R}^p$ when $(X,Y)$ follows a latent factor regression model generat
Externí odkaz:
http://arxiv.org/abs/2007.10050
This work studies finite-sample properties of the risk of the minimum-norm interpolating predictor in high-dimensional regression models. If the effective rank of the covariance matrix $\Sigma$ of the $p$ regression features is much larger than the s
Externí odkaz:
http://arxiv.org/abs/2002.02525
Topic models have become popular tools for dimension reduction and exploratory analysis of text data which consists in observed frequencies of a vocabulary of $p$ words in $n$ documents, stored in a $p\times n$ matrix. The main premise is that the me
Externí odkaz:
http://arxiv.org/abs/2001.07861
Regression models, in which the observed features $X \in \R^p$ and the response $Y \in \R$ depend, jointly, on a lower dimensional, unobserved, latent vector $Z \in \R^K$, with $K< p$, are popular in a large array of applications, and mainly used for
Externí odkaz:
http://arxiv.org/abs/1905.12696