This paper compares FAISS and FENSHSES for nearest neighbor search in Hamming space.
problem Comparing nearest neighbor search systems in Hamming space.
method Comprehensive evaluations of indexing speed, search latency, and RAM consumption.
result Better understanding of trade-offs between main memory and secondary memory systems.
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.
A new multilabel classification framework improves ANN search performance.
problem Efficiently finding approximate nearest neighbors in large datasets.
method Formulated ANN search as a multilabel classification problem, using partitioning classifiers.
result Natural classifier leads to strictly improved performance in ANN search.
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.
This paper improves nearest neighbor search by learning optimal routing functions.
problem Local minima issues in greedy routing on similarity graphs.
method Learn routing function that considers global graph structure.
result Significant improvement in search performance via learning.
Python package reduces hubness in high-dimensional data.
problem Hubness in high-dimensional data affects machine learning tasks.
method Hubness analysis, reduction, and approximate neighbor search.
result Improves neighbor retrieval and learning tasks in high dimensions.
Fast approximate nearest neighbor (NN) search in large databases is becoming popular. Several powerful learning-based formulations have been proposed recently. However, not much attention has been paid to a more fundamental question: how difficult is (approximate) nearest neighbor search in a given data set? And which …
New method improves nearest neighbor search using neural networks and graph partitioning.
problem Efficient nearest neighbor search in high-dimensional spaces.
method Developed a new framework for space partitioning using neural networks and graph partitioning.
result Neural LSH partitions outperform existing methods on standard benchmarks.
Paper improves full-text search engines for fast exact NNS in binary codes.
problem Efficient nearest neighbor search in Hamming space for full-text search engines.
method Revisits and combines three techniques from information retrieval: bit operation, subs-code filtering, and data preprocessing with permutation.
result Significant speed-ups for NNS in binary codes over state-of-the-art term match approach.
Sublinear memory sketch finds nearest neighbors in streaming data.
problem Finding nearest neighbors in large datasets with limited memory.
method Combines LSH, online kernel density estimation, and compressed sensing to achieve sublinear memory.
result Achieves sublinear memory performance on stable queries, reporting nearest neighbors efficiently.
Efficiently selects nearest neighbors for labeling to speed up active learning.
problem Intractable active learning and search for large-scale unlabeled data.
method Restricts candidate pool to nearest neighbors of labeled set.
result Achieved similar performance to global approach but reduced computational cost by up to 3 orders of magnitude.
ForestDSH hashes improve nearest neighbor search in high-dimensional data.
problem High-dimensional classification and nearest neighbor search.
method Distribution-sensitive hashing using a forest of decision trees.
result ForestDSH hashes outperform LSH and state-of-the-art methods in speed and accuracy.
rpForests uses random projections to speed up kNN search.
problem Efficient kNN search in high-dimensional data.
method Random projection forests aggregating results from multiple random projection trees.
result rpForests achieves fast decay in missing rate and kNN distance discrepancy.
New algorithm improves similarity graph construction for nearest neighbor search.
problem Improving nearest neighbor search performance with more effective similarity graphs.
method Probabilistic model of a similarity graph learned through reinforcement learning.
result Higher recall rates achieved for the same number of distance computations.
The paper analyzes how much data points can be altered to change their rank in nearest neighbor searches.
problem Vulnerability of nearest neighbor search in high-dimensional data.
method Statistical analysis of perturbation needed to change neighbor rank.
result Derived statistical distribution of perturbation needed to modify neighbor rank.
This paper provides fast estimates for complex option types.
problem Estimating prices for constrained multiple exercise American options.
method Lookahead search for lower estimates and nearest-neighbor martingale for upper estimates.
result Probabilistic convergence guarantees for the algorithms.
ICQ improves high-dimensional similarity search without sacrificing precision.
problem High-dimensional similarity search is computationally expensive.
method Interleaved Composite Quantization (ICQ) reduces code length and quantization error.
result ICQ achieves fast similarity search without using shorter codes.
Approximate nearest neighbor algorithms are used to speed up nearest neighbor search in a wide array of applications. However, current indexing methods feature several hyperparameters that need to be tuned to reach an acceptable accuracy--speed trade-off. A grid search in the parameter space is often impractically slow…
This paper surveys various methods for dimensionality reduction and nearest neighbor search.
problem Efficiently reducing high-dimensional data to lower dimensions while preserving essential information.
method Linear and nonlinear random projections, including sparse random projections, random Fourier Features, and Random Kitchen Sinks.
result Various methods for dimensionality reduction and nearest neighbor search are explained and compared.
The method of random projections has become a standard tool for machine learning, data mining, and search with massive data at Web scale. The effective use of random projections requires efficient coding schemes for quantizing (real-valued) projected data into integers. In this paper, we focus on a simple 2-bit coding …
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.
Adaptive algorithm speeds up k-nearest-neighbor searches.
problem Finding k nearest neighbors with varying efficiency.
method Adaptive estimation of distances to optimize search.
result The algorithm achieves significant speedups compared to naive methods.
Unsupervised space partitioning improves ANNS performance without pre-processing.
problem Efficient nearest neighbor search in high-dimensional spaces.
method Custom unsupervised learning framework for space partitioning and learning-to-search.
result Our method outperforms state-of-the-art approaches on ANNS benchmarks.
Algorithm finds adversarial examples for k-NN classifiers using Voronoi diagrams.
problem Ensuring robustness of k-NN classifiers against adversarial attacks.
method Geometric approach expanding outwards from input points to find minimum-norm adversarial examples.
result Our method outperforms existing approaches on various datasets.
SANNS secures k-NNS queries and results without revealing them.
problem Securely searching for nearest neighbors without exposing sensitive data.
method Optimized linear scan and clustering-based algorithm with cryptographic primitives.
result SANNS achieves up to 31x faster response times and scales to 10 million entries.
The DEBS Grand Challenge 2018 is set in the context of maritime route prediction. Vessel routes are modeled as streams of Automatic Identification System (AIS) data points selected from real-world tracking data. The challenge requires to correctly estimate the destination ports and arrival times of vessel trips, as ear…
Paper tackles NNS under uncertainty with improved algorithms.
problem Efficient nearest neighbor search with noisy distance estimates.
method Combines cover trees and multi-armed bandits for optimal performance.
result Optimal dependence on dataset size and unknown geometry achieved.
CwA optimizes search performance by jointly learning a balanced database partition and a neural probing function.
problem Suboptimal search performance due to mismatched database and query distributions.
method CwA jointly learns a balanced database partition and a neural probing function to optimize search performance directly for the query distribution.
result CwA achieves up to 4.7x throughput over state-of-the-art methods at equal recall.
New algorithm reconstructs sparse networks in subquadratic time.
problem Reconstructing sparse networks from limited data.
method Stochastic second neighbor search to bypass quadratic complexity.
result Subquadratic time complexity, up to O(N3/2logN). We consider machine learning in a comparison-based setting where we are given a set of points in a metric space, but we have no access to the actual distances between the points. Instead, we can only ask an oracle whether the distance between two points i and j is smaller than the distance between the points i an…
Introduces MFVDM for high-dimensional data analysis.
problem Non-linear dimensionality reduction of high-dimensional datasets.
method Combines multiple unitary irreducible representations for nonlinear embeddings.
result Achieves better nearest neighbor search and alignment estimation on noisy data.
Hashing has been widely used for large-scale approximate nearest neighbor search because of its storage and search efficiency. Recent work has found that deep supervised hashing can significantly outperform non-deep supervised hashing in many applications. However, most existing deep supervised hashing methods adopt a …
Novel co-learning method for manifolds with group actions using multiple fibre bundles.
problem Learning from manifolds with group actions without labeled data.
method Representation theory to associate multiple fibre bundles, leveraging group actions for unsupervised learning.
result Improved robust nearest neighbor search and community detection on cryo-electron microscopy images.
CRAD clusters data with robust depth-based dissimilarity, outperforming existing methods.
problem Clustering data with varying densities and unknown true number of clusters.
method CRAD uses a robust data depth as dissimilarity measure and a novel neighbor searching function.
result CRAD outperforms DBSCAN, OPTICS, and DBCA in detecting clusters with varying densities.
Combines fast evaluation with Bayes consistency in nearest neighbors.
problem Balancing fast evaluation time with Bayes consistency in nearest neighbors.
method Combines locality-sensitive hashing (LSH) with a missing-mass argument.
result Fast and Bayes-consistent classifier with comparable risk decay rates.
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…
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.
Recently it was shown that the problem of Maximum Inner Product Search (MIPS) is efficient and it admits provably sub-linear hashing algorithms. Asymmetric transformations before hashing were the key in solving MIPS which was otherwise hard. In the prior work, the authors use asymmetric transformations which convert th…
Indirect attacks can fool graph classifiers even with poisoned neighbors.
problem How to evaluate and defend graph convolutional neural networks against indirect adversarial attacks.
method Proposed a method to generate adversarial perturbations on a single node far from the target.
result 99% attack success rate within two-hops from the target in two datasets.
Since its introduction, unsupervised representation learning has attracted a lot of attention from the research community, as it is demonstrated to be highly effective and easy-to-apply in tasks such as dimension reduction, clustering, visualization, information retrieval, and semi-supervised learning. In this work, we…
A novel distributed adaptive NN classifier for large data sets.
problem Handling large and distributed data for efficient classification.
method Distributed adaptive nearest neighbor classifier with stochastic tuning parameter selection and early stopping rule.
result Achieves nearly optimal convergence rate under large sub-sample sizes.
The paper investigates learning conditional distributions on multi-dimensional spaces using clustering and neural networks.
problem Learning conditional distributions on multi-dimensional spaces with varying dimensions.
method The approach involves clustering data near varying query points in the feature space to create empirical measures in the target space using two clustering schemes: fixed-radius ball and nearest neighbors. The convergence rates of both methods are analyzed, and the nearest neighbors method is incorporated into neural network training.
result The empirical analysis shows that the nearest neighbors method has better performance in practice and can adapt to a suitable level of Lipschitz continuity locally.
A fast method for finding counterfactual explanations for decision forests.
problem Finding counterfactual explanations for decision forests efficiently.
method Constrain optimization to data-populated regions, reducing to nearest-neighbor search.
result Very fast and realistic counterfactual explanations for decision forests.
We present a simple, yet effective, approach to Semi-Supervised Learning. Our approach is based on estimating density-based distances (DBD) using a shortest path calculation on a graph. These Graph-DBD estimates can then be used in any distance-based supervised learning method, such as Nearest Neighbor methods and SVMs…
Improved causal discovery methods for large graphs without strict assumptions.
problem Sub-optimal solutions due to faithfulness assumption violations.
method Super-structure estimation and local search strategies.
result The proposed method scales to hundreds of nodes with high accuracy.
The Earth Mover's Distance (EMD) is a state-of-the art metric for comparing discrete probability distributions, but its high distinguishability comes at a high cost in computational complexity. Even though linear-complexity approximation algorithms have been proposed to improve its scalability, these algorithms are eit…
Fair near neighbor search ensures equal probability of reporting points in a query's radius.
problem Ensuring equal probability of reporting points within a query's radius.
method LSH-based algorithms modified to ensure uniform probability of reporting points.
result Algorithm reports points in the query's radius with almost uniform probability.
Method detects adversarial samples using influence functions and nearest neighbors.
problem Detecting adversarial attacks on deep neural networks.
method Influence functions and k-NN model on activation layers.
result Successfully distinguishes adversarial examples with state-of-the-art results.