arXiv research

We propose a novel method to accelerate Lloyd's algorithm for K-Means clustering. Unlike previous acceleration approaches that reduce computational cost per iterations or improve initialization, our approach is focused on reducing the number of iterations required for convergence. This is achieved by treating the assig…

A new method for robust product Markovian quantization overcomes numerical instabilities.

LocalKMeans parallelizes Lloyd's algorithm for distributed data.

Clustering is a fundamental problem in statistics and machine learning. Lloyd's algorithm, proposed in 1957, is still possibly the most widely used clustering algorithm in practice due to its simplicity and empirical performance. However, there has been little theoretical investigation on the statistical and computatio…

Fair k-means algorithm ensures equitable costs for different groups.

K-means -- and the celebrated Lloyd algorithm -- is more than the clustering method it was originally designed to be. It has indeed proven pivotal to help increase the speed of many machine learning and data analysis techniques such as indexing, nearest-neighbor search and prediction, data compression; its beneficial u…

K-means fails catastrophically in high dimensions, Hartigan's avoids it.

The Lloyd-Max algorithm is a classical approach to perform K-means clustering. Unfortunately, its cost becomes prohibitive as the training dataset grows large. We propose a compressive version of K-means (CKM), that estimates cluster centers from a sketch, i.e. from a drastically compressed representation of the traini…

Paper establishes universal lower bounds and optimal rates for clustering sub-exponential mixture models.

K-bMOM robustly clusters data with outliers, improving on Lloyd-type methods.

Research simulates Lloyd's of London's specialty insurance market dynamics.

New algorithms cluster nodes in SBM graphs faster and more accurately.

Using a trimming approach, we investigate a k-means type method based on Bregman divergences for clustering data possibly corrupted with clutter noise. The main interest of Bregman divergences is that the standard Lloyd algorithm adapts to these distortion measures, and they are well-suited for clustering data sampled …

The paper provides convergence bounds for approximating a distribution using point clouds.

We consider $K$-means clustering in networked environments (e.g., internet of things (IoT) and sensor networks) where data is inherently distributed across nodes and processing power at each node may be limited. We consider a clustering algorithm referred to as networked $K$-means, or $NK$-means, which relies only on l…

A method uses Wasserstein clustering to simplify financial data analysis.

Paper proposes efficient methods for high-order clustering in tensor block models.

A new heuristic LM algorithm improves $k$-segmentation accuracy with less computation.

One iteration of standard $k$-means (i.e., Lloyd's algorithm) or standard EM for Gaussian mixture models (GMMs) scales linearly with the number of clusters $C$, data points $N$, and data dimensionality $D$. In this study, we explore whether one iteration of $k$-means or EM for GMMs can scale sublinearly with $C$ at run…

In this paper, we study the problem of learning a mixture of Gaussians with streaming data: given a stream of $N$ points in $d$ dimensions generated by an unknown mixture of $k$ spherical Gaussians, the goal is to estimate the model parameters using a single pass over the data stream. We analyze a streaming version of …

We propose a general modeling and algorithmic framework for discrete structure recovery that can be applied to a wide range of problems. Under this framework, we are able to study the recovery of clustering labels, ranks of players, signs of regression coefficients, cyclic shifts, and even group elements from a unified…

Optimizes clustering in Gaussian mixtures with varying covariance matrices.

K-means fails in high dimensions with noise and few samples.

This paper studies accelerations in Q-learning algorithms. We propose an accelerated target update scheme by incorporating the historical iterates of Q functions. The idea is conceptually inspired by the momentum-based accelerated methods in the optimization theory. Conditions under which the proposed accelerated algor…

Super-acceleration of gradient descent with momentum improves loss function minimization.

Hamiltonian dynamics-based algorithms achieve deterministic and accelerated convergence for convex optimization.

Continuized Nesterov acceleration accelerates stochastic gradient descent and gossip algorithms.

Two new differentially private optimization algorithms derived from accelerated methods.

We present an accelerated algorithm for hierarchical density based clustering. Our new algorithm improves upon HDBSCAN*, which itself provided a significant qualitative improvement over the popular DBSCAN algorithm. The accelerated HDBSCAN* algorithm provides comparable performance to DBSCAN, while supporting variable …

Octagon map accelerates diagonal changes algorithm.

PF-LaCG removes the need for knowing smoothness and strong convexity parameters for locally accelerated CG.

Improved analysis of accelerated noisy power method for PCA.

New algorithm accelerates optimization on Riemannian manifolds, including Wasserstein space.

Accelerated gradient method's stability deteriorates exponentially with steps.

Accelerates sampling from Gibbs distributions using ARWP method.

We propose accelerated randomized coordinate descent algorithms for stochastic optimization and online learning. Our algorithms have significantly less per-iteration complexity than the known accelerated gradient algorithms. The proposed algorithms for online learning have better regret performance than the known rando…

Recursive Marginal Quantization (RMQ) allows fast approximation of solutions to stochastic differential equations in one-dimension. When applied to two factor models, RMQ is inefficient due to the fact that the optimization problem is usually performed using stochastic methods, e.g., Lloyd's algorithm or Competitive Le…

Paper discovers structural dynamics equations from only acceleration data.

In this paper, we propose a general framework to accelerate significantly the algorithms for nonnegative matrix factorization (NMF). This framework is inspired from the extrapolation scheme used to accelerate gradient methods in convex optimization and from the method of parallel tangents. However, the use of extrapola…

New algorithms accelerate MAP inference in Markov fields with faster convergence.

Accelerates machine learning algorithms for sparse data.

Accelerates policy optimization in RL with optimistic and adaptive updates.

Regularized nonlinear acceleration (RNA) estimates the minimum of a function by post-processing iterates from an algorithm such as the gradient method. It can be seen as a regularized version of Anderson acceleration, a classical acceleration scheme from numerical analysis. The new scheme provably improves the rate of …

HF-opt uses Hamiltonian dynamics to optimize functions, achieving accelerated rates with randomized integration time.

Anderson acceleration (or Anderson mixing) is an efficient acceleration method for fixed point iterations $x_{t+1}=G(x_t)$, e.g., gradient descent can be viewed as iteratively applying the operation $G(x) \triangleq x-α\nabla f(x)$. It is known that Anderson acceleration is quite efficient in practice and can be viewed…

Quantum computers can optimize foreign exchange reserves management.

Accelerates TD learning for long-horizon reinforcement learning problems.

Improved first-order algorithm for entropy regularized OT with faster convergence.