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Found 12 papers, 4 papers with code
A Statistical Analysis of Wasserstein Autoencoders for Intrinsically Low-dimensional Data
To bridge the gap between the theory and practice of WAEs, in this paper, we show that WAEs can learn the data distributions when the network architectures are properly chosen.
On the Statistical Properties of Generative Adversarial Models for Low Intrinsic Data Dimension
In this paper, we attempt to bridge the gap between the theory and practice of GANs and their bidirectional variant, Bi-directional GANs (BiGANs), by deriving statistical guarantees on the estimated densities in terms of the intrinsic dimension of the data and the latent space.
Bregman Power k-Means for Clustering Exponential Family Data
Recent progress in center-based clustering algorithms combats poor local minima by implicit annealing, using a family of generalized means.
Robust Linear Predictions: Analyses of Uniform Concentration, Fast Rates and Model Misspecification
The problem of linear predictions has been extensively studied for the past century under pretty generalized frameworks.
Uniform Concentration Bounds toward a Unified Framework for Robust Clustering
Recent advances in center-based clustering continue to improve upon the drawbacks of Lloyd's celebrated $k$-means algorithm over $60$ years after its introduction.
Robust Principal Component Analysis: A Median of Means Approach
Principal Component Analysis (PCA) is a fundamental tool for data visualization, denoising, and dimensionality reduction.
Automated Clustering of High-dimensional Data with a Feature Weighted Mean Shift Algorithm
Mean shift is a simple interactive procedure that gradually shifts data points towards the mode which denotes the highest density of data points in the region.
Kernel k-Means, By All Means: Algorithms and Strong Consistency
We show the method implicitly performs annealing in kernel feature space while retaining efficient, closed-form updates, and we rigorously characterize its convergence properties both from computational and statistical points of view.
Principal Ellipsoid Analysis (PEA): Efficient non-linear dimension reduction & clustering
In a rich variety of real data clustering applications, PEA is shown to do as well as k-means for simple datasets, while dramatically improving performance in more complex settings.
Entropy Regularized Power k-Means Clustering
Despite its well-known shortcomings, $k$-means remains one of the most widely used approaches to data clustering.
A Strongly Consistent Sparse $k$-means Clustering with Direct $l_1$ Penalization on Variable Weights
We propose the Lasso Weighted $k$-means ($LW$-$k$-means) algorithm as a simple yet efficient sparse clustering procedure for high-dimensional data where the number of features ($p$) can be much larger compared to the number of observations ($n$).
An Overview of Face Liveness Detection
The main aim is to provide a simple path for the future development of novel and more secured face liveness detection approach.
