arXiv research

The paper proposes a soil pH prediction method using nearest fields.

The nearest neighbor problem is defined as follows: Given a set $P$ of $n$ points in some metric space $(X,D)$, build a data structure that, given any point $q$, returns a point in $P$ that is closest to $q$ (its "nearest neighbor" in $P$). The data structure stores additional information about the set $P$, which is th…

Generative models use kernel smoothing for conditioning on small example sets.

Prior distributions of binarized natural images are learned by using a Boltzmann machine. According the results of this study, there emerges a structure with two sublattices in the interactions, and the nearest-neighbor and next-nearest-neighbor interactions correspondingly take two discriminative values, which reflect…

New algorithm improves topological stability in non-linear dimensionality reduction.

Study nearest-neighbor radii under dependent sampling, finding they remain informative.

Deep nearest neighbors outperform self-supervised methods in anomaly detection.

Characterizes Lebesgue points using nearest neighbor methods.

The paper explains how nearest neighbor methods succeed in prediction.

AWNN improves matrix completion by adaptively weighting nearest neighbors.

We study the persistence phenomenon in a socio-econo dynamics model using computer simulations at a finite temperature on hypercubic lattices in dimensions up to 5. The model includes a ` social\rq local field which contains the magnetization at time $t$. The nearest neighbour quenched interactions are drawn from a bin…

A new method uses nearest neighbors for importance weighting.

The estimation of optimal treatment regimes is of considerable interest to precision medicine. In this work, we propose a causal $k$-nearest neighbor method to estimate the optimal treatment regime. The method roots in the framework of causal inference, and estimates the causal treatment effects within the nearest neig…

A review of nearest neighbour classifiers, focusing on similarity measures and computational efficiency.

Nearest neighbor methods are a popular class of nonparametric estimators with several desirable properties, such as adaptivity to different distance scales in different regions of space. Prior work on convergence rates for nearest neighbor classification has not fully reflected these subtle properties. We analyze the b…

Neural networks classify OOD images by their nearest neighbor in training data.

VNNGP uses nearest neighbors to approximate GPs, improving scalability and performance.

Java implementation improves nearest neighbor algorithm complexity.

The paper tackles time series data by applying conformal prediction with nearest neighbors.

A new multilabel classification framework improves ANN search performance.

K-Nearest neighbor classifier (k-NNC) is simple to use and has little design time like finding k values in k-nearest neighbor classifier, hence these are suitable to work with dynamically varying data-sets. There exists some fundamental improvements over the basic k-NNC, like weighted k-nearest neighbors classifier (wh…

The paper studies empirical processes from nearest neighbors in regression.

New bounds for balanced classification improve understanding of imbalanced datasets.

This paper proposes a new hashing-based KNN technique for faster nearest neighbor selection.

In this paper we propose and explore the k-Nearest Neighbour UCB algorithm for multi-armed bandits with covariates. We focus on a setting where the covariates are supported on a metric space of low intrinsic dimension, such as a manifold embedded within a high dimensional ambient feature space. The algorithm is concept…

A new adaptive kNN classifier outperforms Random Forests.

New algorithm reduces regret in contextual bandits with many near-boundary contexts.

Sparse nearest-centroid classifiers detect relevant features for classification.

Locally adaptive nearest neighbors improve automated systems' performance and are easier to interpret.

Paper explains distance-based classifiers using neural network structures.

New methods use vector search and nearest-neighbor matching for policy learning in causal inference.

New method speeds up k-means clustering for large k by improving nearest-neighbor search.

We study the persistence phenomenon in a socio-econo dynamics model using computer simulations at a finite temperature on hypercubic lattices in dimensions up to 5. The model includes a ` social\rq local field which contains the magnetization at time $t$. The nearest neighbour quenched interactions are drawn from a bin…

Improved multiclass classification with class-weighted nearest neighbors.

This paper functions as a tutorial for individuals interested to enter the field of information retrieval but wouldn't know where to begin from. It describes two fundamental yet efficient image retrieval techniques, the first being k - nearest neighbors (knn) and the second support vector machines(svm). The goal is to …

A hybrid K-NN and SVM technique improves classification accuracy.

Algorithms often carry out equally many computations for "easy" and "hard" problem instances. In particular, algorithms for finding nearest neighbors typically have the same running time regardless of the particular problem instance. In this paper, we consider the approximate k-nearest-neighbor problem, which is the pr…

Combines fast evaluation with Bayes consistency in nearest neighbors.

Study shows $k$-NN regressor consistency in complex survey designs.

In this paper, we compare the performances of FAISS and FENSHSES on nearest neighbor search in Hamming space--a fundamental task with ubiquitous applications in nowadays eCommerce. Comprehensive evaluations are made in terms of indexing speed, search latency and RAM consumption. This comparison is conducted towards a b…

Study detects SLI in children from spontaneous narrative transcripts.

New method uses weighting vectors for efficient boundary and outlier detection.

This paper presents a simple but effective density-based outlier detection approach with the local kernel density estimation (KDE). A Relative Density-based Outlier Score (RDOS) is introduced to measure the local outlierness of objects, in which the density distribution at the location of an object is estimated with a …

We propose a simple kernel based nearest neighbor approach for handwritten digit classification. The "distance" here is actually a kernel defining the similarity between two images. We carefully study the effects of different number of neighbors and weight schemes and report the results. With only a few nearest neighbo…

In the panoply of pattern classification techniques, few enjoy the intuitive appeal and simplicity of the nearest neighbor rule: given a set of samples in some metric domain space whose value under some function is known, we estimate the function anywhere in the domain by giving the value of the nearest sample per the …

The stability of statistical analysis is an important indicator for reproducibility, which is one main principle of scientific method. It entails that similar statistical conclusions can be reached based on independent samples from the same underlying population. In this paper, we introduce a general measure of classif…

In many scientific disciplines structures in high-dimensional data have to be found, e.g., in stellar spectra, in genome data, or in face recognition tasks. In this work we present a novel approach to non-linear dimensionality reduction. It is based on fitting K-nearest neighbor regression to the unsupervised regressio…

The condensed nearest neighbor (CNN) algorithm is a heuristic for reducing the number of prototypical points stored by a nearest neighbor classifier, while keeping the classification rule given by the reduced prototypical set consistent with the full set. I present an upper bound on the number of prototypical points ac…