arXiv research

Paper proposes MS-k-NN for improved convergence rate in k-NN classification.

In the $k$-nearest neighborhood model ($k$-NN), we are given a set of points $P$, and we shall answer queries $q$ by returning the $k$ nearest neighbors of $q$ in $P$ according to some metric. This concept is crucial in many areas of data analysis and data processing, e.g., computer vision, document retrieval and machi…

We derive high-probability finite-sample uniform rates of consistency for $k$-NN regression that are optimal up to logarithmic factors under mild assumptions. We moreover show that $k$-NN regression adapts to an unknown lower intrinsic dimension automatically. We then apply the $k$-NN regression rates to establish new …

The $k$-nearest neighbor classification method ($k$-NNC) is one of the simplest nonparametric classification methods. The mutual $k$-NN classification method (M$k$NNC) is a variant of $k$-NNC based on mutual neighborship. We propose another variant of $k$-NNC, the symmetric $k$-NN classification method (S$k$NNC) based …

A new method for learning distance metrics for K-NN classification.

Implementing $k$-NN classification using Gromov--Wasserstein distances

Enhances k-NN accuracy through randomized hyperstructure.

A fast method for LOOCV in k-NN regression reduces computation time.

The $k$-nearest neighbour ($k$-NN) classifier is one of the oldest and most important supervised learning algorithms for classifying datasets. Traditionally the Euclidean norm is used as the distance for the $k$-NN classifier. In this thesis we investigate the use of alternative distances for the $k$-NN classifier. We …

Study shows $k$-NN classifier is not universally consistent on $(0,1)$ but consistent on discrete and specific measure spaces.

Improved OOD detection using label smoothing and k-NN density estimates.

A novel k-NN method estimates conditional mean and variance efficiently.

New method improves crowd counting accuracy using inverse k-NN maps and multiscale upsampling.

Multiple classifier systems focus on the combination of classifiers to obtain better performance than a single robust one. These systems unfold three major phases: pool generation, selection and integration. One of the most promising MCS approaches is Dynamic Selection (DS), which relies on finding the most competent c…

The $k$-NN graph has played a central role in increasingly popular data-driven techniques for various learning and vision tasks; yet, finding an efficient and effective way to construct $k$-NN graphs remains a challenge, especially for large-scale high-dimensional data. In this paper, we propose a new approach to const…

The paper optimizes k-NN for distributed learning with minimax optimal performance.

Image-based malware detection using transfer learning outperforms simple k-NN.

We investigate the classification performance of K-nearest neighbors (K-NN) and deep neural networks (DNNs) in the presence of label noise. We first show empirically that a DNN's prediction for a given test example depends on the labels of the training examples in its local neighborhood. This motivates us to derive a r…

We propose a simple approach which, given distributed computing resources, can nearly achieve the accuracy of $k$-NN prediction, while matching (or improving) the faster prediction time of $1$-NN. The approach consists of aggregating denoised $1$-NN predictors over a small number of distributed subsamples. We show, bot…

Paper develops robust $k$-NN algorithm for few samples.

This paper studies the relationship between the classification performed by deep neural networks (DNNs) and the decision of various classical classifiers, namely k-nearest neighbours (k-NN), support vector machines (SVM) and logistic regression (LR), at various layers of the network. This comparison provides us with ne…

Simple k-NN filtering improves model accuracy on noisy labels.

$k$ Nearest Neighbors ($k$NN) is one of the most widely used supervised learning algorithms to classify Gaussian distributed data, but it does not achieve good results when it is applied to nonlinear manifold distributed data, especially when a very limited amount of labeled samples are available. In this paper, we pro…

Improved convergence rate for kNN graph Laplacians with adaptive bandwidth.

Improves k-NN for monotonic data with robustness against noise.

The problem of supervised classification (or discrimination) with functional data is considered, with a special interest on the popular k-nearest neighbors (k-NN) classifier. First, relying on a recent result by Cerou and Guyader (2006), we prove the consistency of the k-NN classifier for functional data whose distribu…

Proposes LRR and LRLR for improving stock prediction accuracy.

Combines k-NN and RVM for improved classification accuracy.

Estimating entropy and mutual information consistently is important for many machine learning applications. The Kozachenko-Leonenko (KL) estimator (Kozachenko & Leonenko, 1987) is a widely used nonparametric estimator for the entropy of multivariate continuous random variables, as well as the basis of the mutual inform…

Adaptive k-NN classifier improves accuracy over fixed k-NN.

Prototype rules simplify multiclass classification in metric spaces, achieving consistency and reduced complexity.

PAC-Bayesian bounds improve understanding of K-NN classifier performance.

Consistency of k-NN rule proven in sigma-finite dimensional metric spaces.

Introduction. Case Based Reasoning (CBR) is an emerg- ing decision making paradigm in medical research where new cases are solved relying on previously solved similar cases. Usually, a database of solved cases is provided, and every case is described through a set of attributes (inputs) and a label (output). Extracting…

Paper uses K-NN resampling to simulate and evaluate LOB markets.

Paper improves $k$-NN predictive performance with efficient variable selection.

Study shows SNN graph Laplacians converge to k-NN graph Laplacians under large scale asymptotics.

We propose a non-parametric anomaly detection algorithm for high dimensional data. We score each datapoint by its average $K$-NN distance, and rank them accordingly. We then train limited complexity models to imitate these scores based on the max-margin learning-to-rank framework. A test-point is declared as an anomaly…

The paper improves spectral convergence rates for graph Laplacians.

Improved gesture recognition using compressed domain signals.

We investigate nearest neighbor and generative models for transferring pose between persons. We take in a video of one person performing a sequence of actions and attempt to generate a video of another person performing the same actions. Our generative model (pix2pix) outperforms k-NN at both generating corresponding f…

A new strategy selects k in k-NN regression without hold-out data.

From a fresh data science perspective, this thesis discusses the prediction of coronary artery disease based on genetic variations at the DNA base pair level, called Single-Nucleotide Polymorphisms (SNPs), collected from the Ontario Heart Genomics Study (OHGS). First, the thesis explains two commonly used supervised le…

This paper introduces a class of k-nearest neighbor ($k$-NN) estimators called bipartite plug-in (BPI) estimators for estimating integrals of non-linear functions of a probability density, such as Shannon entropy and Rényi entropy. The density is assumed to be smooth, have bounded support, and be uniformly bounded from…

A new $k$-NN algorithm using surprisal for robust and interpretable nonparametric learning.

New method learns local metrics for k-NN classification using sample similarity.

Improved k-NN active learning with local smoothness assumption.

BaNk-UCB tackles batched nonparametric bandits with k-NN regression and UCB.