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Found 15 papers, 4 papers with code
Sketch-and-solve approaches to k-means clustering by semidefinite programming
This lower bound is data-driven; it does not make any assumption on the data nor how it is generated.
Group-invariant max filtering
Given a real inner product space $V$ and a group $G$ of linear isometries, we construct a family of $G$-invariant real-valued functions on $V$ that we call max filters.
Neural collapse with unconstrained features
Neural collapse is an emergent phenomenon in deep learning that was recently discovered by Papyan, Han and Donoho.
Lie PCA: Density estimation for symmetric manifolds
In particular, we use a spectral method to approximate the Lie algebra corresponding to the symmetry group of the underlying manifold.
Sketching semidefinite programs for faster clustering
Many clustering problems enjoy solutions by semidefinite programming.
SqueezeFit: Label-aware dimensionality reduction by semidefinite programming
Given labeled points in a high-dimensional vector space, we seek a low-dimensional subspace such that projecting onto this subspace maintains some prescribed distance between points of differing labels.
SUNLayer: Stable denoising with generative networks
It has been experimentally established that deep neural networks can be used to produce good generative models for real world data.
Monte Carlo approximation certificates for k-means clustering
Efficient algorithms for $k$-means clustering frequently converge to suboptimal partitions, and given a partition, it is difficult to detect $k$-means optimality.
Clustering subgaussian mixtures by semidefinite programming
We introduce a model-free relax-and-round algorithm for k-means clustering based on a semidefinite relaxation due to Peng and Wei.
Probably certifiably correct k-means clustering
First, we prove that Peng and Wei's semidefinite relaxation of k-means is tight with high probability under a distribution of planted clusters called the stochastic ball model.
Learning Boolean functions with concentrated spectra
This paper discusses the theory and application of learning Boolean functions that are concentrated in the Fourier domain.
On the tightness of an SDP relaxation of k-means
Recently, Awasthi et al. introduced an SDP relaxation of the $k$-means problem in $\mathbb R^m$.
Compressive classification and the rare eclipse problem
This paper addresses the fundamental question of when convex sets remain disjoint after random projection.
Constructing all self-adjoint matrices with prescribed spectrum and diagonal
In this paper, we show how to explicitly construct every such sequence of eigensteps.
Functional Analysis 42C15
Constructing finite frames of a given spectrum and set of lengths
When constructing finite frames for a given application, the most important consideration is the spectrum of the frame operator.
Functional Analysis
