We explore and expand the to measure the of class manifolds in representation space: i.e., how close pairs of points from the same class are relative to pairs of points from different classes. We demonstrate several use cases of the loss. As an analytical to…
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48 results for “Soft Nearest Neighbor”
Method generates prototypes from small datasets for efficient learning.
problem Efficiently learning from small datasets with soft labels.
method Modular method for generating soft-label prototypical lines and Hierarchical Soft-Label Prototype k-Nearest Neighbor algorithm.
result High classification accuracy with significantly fewer prototypes than classes.
Less-than-one-shot learning tackles few-shot learning with minimal data.
problem Training models on very small datasets while maintaining accuracy.
method Soft-label k-Nearest Neighbors classifier and theoretical lower bounds analysis.
result Achieving learning of multiple classes with fewer than the required samples.
Improved clustering accuracy with disentangled latent code representation.
problem Improving k-Means clustering performance.
method Optimizing the entanglement of autoencoder latent code representation using soft nearest neighbor loss with annealing temperature.
result 96.2% test clustering accuracy on MNIST, 85.6% on Fashion-MNIST, and 79.2% on EMNIST Balanced datasets.
Improved KNN data valuation method with reduced computation time.
problem Efficiently valuing individual data points in KNN models.
method Proposed a new utility function and derived its calculation for KNN classifiers/regressors, achieving similar time complexity as the original method.
result Soft-label KNN-SV outperforms the original method in mislabeled data detection.
Study nearest-neighbor radii under dependent sampling, finding they remain informative.
problem Analyzing nearest-neighbor radii under dependent sampling.
method Consider strong mixing dependent observations, establish distribution-free almost sure convergence and sharp non-asymptotic moment bounds.
result Nearest-neighbor geometry remains informative under dependence sampling.
Characterizes Lebesgue points using nearest neighbor methods.
problem Consistency of classification algorithms based on nearest neighbors.
method Characterization of Lebesgue points via 1-Nearest Neighbor regression.
result Proves convergence of 1-Nearest Neighbor classification algorithms in metric spaces.
The paper explains how nearest neighbor methods succeed in prediction.
problem Explaining the success of nearest neighbor methods in prediction.
method The paper covers both theoretical and practical aspects of nearest neighbor methods, including statistical guarantees and practical algorithms.
result The paper provides nonasymptotic statistical guarantees and practical algorithms for nearest neighbor methods.
AWNN improves matrix completion by adaptively weighting nearest neighbors.
problem Matrix completion with optimal nearest neighbor weights and radii selection.
method Adaptively weighted nearest neighbor method for matrix completion.
result Theoretical guarantees and synthetic experiments support the effectiveness of AWNN.
A new method uses nearest neighbors for importance weighting.
problem Data covariate shift problems in machine learning.
method Nearest neighbor classification scheme for determining importance weights.
result Demonstrated effectiveness through comparative experiments on various classification tasks.
The distance metric plays an important role in nearest neighbor (NN) classification. Usually the Euclidean distance metric is assumed or a Mahalanobis distance metric is optimized to improve the NN performance. In this paper, we study the problem of embedding arbitrary metric spaces into a Euclidean space with the goal…
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…
The estimation of optimal treatment regimes is of considerable interest to precision medicine. In this work, we propose a causal -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…
VNNGP uses nearest neighbors to approximate GPs, improving scalability and performance.
problem Scalability issues in Gaussian process approximations.
method Sparse precision structure via nearest neighbors, variational framework.
result VNNGP outperforms low-rank methods and is less prone to overfitting.
Nearest neighbors is a successful and long-standing technique for anomaly detection. Significant progress has been recently achieved by self-supervised deep methods (e.g. RotNet). Self-supervised features however typically under-perform Imagenet pre-trained features. In this work, we investigate whether the recent prog…
Java implementation improves nearest neighbor algorithm complexity.
problem Improving efficiency of nearest neighbor descent algorithm.
method Parallel streams implementation with statistical termination criterion.
result Complexity up to for -nearest neighbors.
The paper tackles time series data by applying conformal prediction with nearest neighbors.
problem Time series data violates the exchangeability assumption required for conformal prediction.
method The approach uses the nearest neighbors method with fast parameter tuning and weighted nearest neighbors (FPTO-WNN) to construct reliable prediction intervals.
result Data analysis shows the effectiveness of the proposed approach.
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.
problem Estimating conditional cumulative distribution functions and local linear regression.
method Uniform central limit theorem and non-asymptotic bound under local bracketing entropy and uniform entropy numbers.
result Gaussian limit of empirical process with simple covariance.
This paper proposes a new hashing-based KNN technique for faster nearest neighbor selection.
problem Slowness of KNN in big datasets due to searching entire dataset.
method Divide data space into subcells, use hashing to map data points, and select nearest neighbors layer by layer.
result The proposed technique offers competitive performance with KNN and KDtree while significantly improving time efficiency.
New methods use vector search and nearest-neighbor matching for policy learning in causal inference.
problem Learning optimal policies in causal inference with limited data.
method RAG-based policy learning with vector search and nearest-neighbor matching.
result The methods bound the within-candidate choice regret and evaluate the one-step method directly as a policy.
The nearest neighbor problem is defined as follows: Given a set of points in some metric space , build a data structure that, given any point , returns a point in that is closest to (its "nearest neighbor" in ). The data structure stores additional information about the set , which is th…
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…
Locally adaptive nearest neighbors improve automated systems' performance and are easier to interpret.
problem Improving automated systems' performance and interpretability.
method Developed a method for k nearest neighbors algorithms to learn locally adaptive metrics.
result Locally adaptive metrics improve performance and are interpretable.
Both supervised and unsupervised machine learning algorithms have been used to learn partition-based index structures for approximate nearest neighbor (ANN) search. Existing supervised algorithms formulate the learning task as finding a partition in which the nearest neighbors of a training set point belong to the same…
New method speeds up k-means clustering for large k by improving nearest-neighbor search.
problem Efficiently clustering large datasets with high-dimensional points.
method Seeded Approximate Nearest-Neighbor Search methods to improve Lloyd's algorithm.
result Significantly faster k-means clustering for large k values.
Improved multiclass classification with class-weighted nearest neighbors.
problem Multiclass classification with large or imbalanced classes.
method Class-weighted k-nearest neighbors algorithm, derived bounds on accuracy and risk.
result Optimized classification metrics like F1 score or Matthew's Correlation Coefficient.
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…
Study shows -NN regressor consistency in complex survey designs.
problem Lack of consistency results for -NN regressor in complex survey data.
method Analysis of regularity conditions on sampling design and data distribution.
result Consistency of -NN regressor under 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…
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 …
Nearest neighbor is a popular class of classification methods with many desirable properties. For a large data set which cannot be loaded into the memory of a single machine due to computation, communication, privacy, or ownership limitations, we consider the divide and conquer scheme: the entire data set is divided in…
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…
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…
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…
Deep neural networks (DNNs) enable innovative applications of machine learning like image recognition, machine translation, or malware detection. However, deep learning is often criticized for its lack of robustness in adversarial settings (e.g., vulnerability to adversarial inputs) and general inability to rationalize…
The top-k operation, i.e., finding the k largest or smallest elements from a collection of scores, is an important model component, which is widely used in information retrieval, machine learning, and data mining. However, if the top-k operation is implemented in an algorithmic way, e.g., using bubble algorithm, the re…
This research examines how the error rate of nearest neighbor classifiers varies with dataset size.
problem The scaling of classification error rates with dataset size is not uniform.
method Theoretical analysis of nearest neighbor classifiers, focusing on early and late phases of dataset size.
result The error rate of nearest neighbor classifiers can have fine-grained rates depending on the dataset size and data distribution.
The nearest neighbor rule is proven consistent in a broad setting.
problem Proving consistency of the nearest neighbor rule in various settings.
method Proving online consistency for all measurable functions in doubling metric spaces under mild assumptions.
result The nearest neighbor rule is online consistent in all measurable functions in doubling metric spaces.
The nearest neighbor classifier fails in high dimensions, leading to this study.
problem Failure of nearest neighbor classifier in high-dimensional data.
method Discussed and proposed new methods to address the issue.
result The proposed methods improve performance in high-dimensional data.
If we pick random points uniformly in and connect each point to its nearest neighbors, then it is well known that there exists a giant connected component with high probability. We prove that in it suffices to connect every point to points chosen randomly among its $…
New method accelerates large margin metric learning for nearest neighbor classification.
problem Efficiently learning metrics for nearest neighbor classification.
method Triplet mining and stratified sampling for large margin metric learning.
result Improved efficiency and scalability of optimization.
Estimates missing distributions using nearest neighbors with kernel methods.
problem Missing data and unobserved confounding in multivariate distributions.
method Distributional matrix completion framework with kernel nearest neighbors.
result Consistent recovery of underlying distributions with missing data.
The nearest neighbor method together with the dynamic time warping (DTW) distance is one of the most popular approaches in time series classification. This method suffers from high storage and computation requirements for large training sets. As a solution to both drawbacks, this article extends learning vector quantiz…
We propose Bayesian extensions of two nonparametric regression methods which are kernel and mutual -nearest neighbor regression methods. Derived based on Gaussian process models for regression, the extensions provide distributions for target value estimates and the framework to select the hyperparameters. It is show…
Under-bagging -NN improves performance on imbalanced classification.
problem Imbalanced classification problems where one class is significantly underrepresented.
method Proposes an under-bagging -NN ensemble learning algorithm, analyzing convergence rates and efficiency.
result Achieves optimal convergence rates under mild assumptions and reduces sub-sample size and for highly imbalanced data.
When using the K-nearest neighbors method, one often ignores uncertainty in the choice of K. To account for such uncertainty, Holmes and Adams (2002) proposed a Bayesian framework for K-nearest neighbors (KNN). Their Bayesian KNN (BKNN) approach uses a pseudo-likelihood function, and standard Markov chain Monte Carlo (…
Study shows SNN graph Laplacians converge to k-NN graph Laplacians under large scale asymptotics.
problem Understanding the convergence of SNN graph Laplacians to k-NN graph Laplacians.
method Analyzing the asymptotic behavior of SNN and k-NN graph Laplacians.
result The graph Laplacians of SNN and k-NN graphs converge to the same limit under large scale asymptotics.