A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.
The paper optimizes k-NN for distributed learning with minimax optimal performance.
Consistency of k-NN rule proven in sigma-finite dimensional metric spaces.
Prototype rules simplify multiclass classification in metric spaces, achieving consistency and reduced complexity.
-NN classifier is one of the most famous classification algorithms, whose performance is crucially dependent on the distance metric. When we consider the distance metric as a parameter of -NN, learning an appropriate distance metric for -NN can be seen as minimizing the empirical risk of -NN. In this paper,…
Learning rule consistency tied to non-existence of real-valued measurable cardinals.
This study combines two different learning paradigms, k-nearest neighbor (k-NN) rule, as memory-based learning paradigm and relevance vector machines (RVM), as statistical learning paradigm. This combination is performed in kernel space and is called k-relevance vector (k-RV). The purpose is to improve the performance …
A novel k-NN method estimates conditional mean and variance efficiently.
The -nearest neighbour (-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 -NN classifier. In this thesis we investigate the use of alternative distances for the -NN classifier. We …
In the -nearest neighborhood model (-NN), we are given a set of points , and we shall answer queries by returning the nearest neighbors of in 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…
Relevance vector machine (RVM) can be seen as a probabilistic version of support vector machines which is able to produce sparse solutions by linearly weighting a small number of basis functions instead using all of them. Regardless of a few merits of RVM such as giving probabilistic predictions and relax of parameter …
Paper develops robust -NN algorithm for few samples.
The -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 -NN graphs remains a challenge, especially for large-scale high-dimensional data. In this paper, we propose a new approach to const…
Study shows -NN classifier is not universally consistent on but consistent on discrete and specific measure spaces.
Simple k-NN filtering improves model accuracy on noisy labels.
We derive high-probability finite-sample uniform rates of consistency for -NN regression that are optimal up to logarithmic factors under mild assumptions. We moreover show that -NN regression adapts to an unknown lower intrinsic dimension automatically. We then apply the -NN regression rates to establish new …
The -nearest neighbor classification method (-NNC) is one of the simplest nonparametric classification methods. The mutual -NN classification method (MNNC) is a variant of -NNC based on mutual neighborship. We propose another variant of -NNC, the symmetric -NN classification method (SNNC) based …
Implementing -NN classification using Gromov--Wasserstein distances
A fast method for LOOCV in k-NN regression reduces computation time.
-nearest neighbour (-NN) is one of the simplest and most widely-used methods for supervised classification, that predicts a query's label by taking weighted ratio of observed labels of objects nearest to the query. The weights and the parameter regulate its bias-variance trade-off, and the …
Nearest Neighbors (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…
In this paper, we consider the problem of malware detection and classification based on image analysis. We convert executable files to images and apply image recognition using deep learning (DL) models. To train these models, we employ transfer learning based on existing DL models that have been pre-trained on massive …
Proposes LRR and LRLR for improving stock prediction 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…
Improved OOD detection using label smoothing and k-NN density estimates.
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…
Paper improves -NN predictive performance with efficient variable selection.
We propose a non-parametric anomaly detection algorithm for high dimensional data. We score each datapoint by its average -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…
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…
In the k-nearest neighbor algorithm (k-NN), the determination of classes for test instances is usually performed via a majority vote system, which may ignore the similarities among data. In this research, the researcher proposes an approach to fine-tune the selection of neighbors to be passed to the majority vote syste…
PAC-Bayesian bounds improve understanding of K-NN classifier performance.
We propose a simple approach which, given distributed computing resources, can nearly achieve the accuracy of -NN prediction, while matching (or improving) the faster prediction time of -NN. The approach consists of aggregating denoised -NN predictors over a small number of distributed subsamples. We show, bot…
A new -NN algorithm using surprisal for robust and interpretable nonparametric learning.
Previously, we proposed a physically inspired rule to organize the data points in a sparse yet effective structure, called the in-tree (IT) graph, which is able to capture a wide class of underlying cluster structures in the datasets, especially for the density-based datasets. Although there are some redundant edges or…
Paper uses K-NN resampling to simulate and evaluate LOB markets.
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…
This paper proposes an inexpensive way to learn an effective dissimilarity function to be used for -nearest neighbor (-NN) classification. Unlike Mahalanobis metric learning methods that map both query (unlabeled) objects and labeled objects to new coordinates by a single transformation, our method learns a trans…
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…
We develop a prediction-based prescriptive model for learning optimal personalized treatments for patients based on their Electronic Health Records (EHRs). Our approach consists of: (i) predicting future outcomes under each possible therapy using a robustified nonlinear model, and (ii) adopting a randomized prescriptiv…
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…
Under-bagging -NN improves performance on imbalanced classification.
Gatherings of thousands to millions of people frequently occur for an enormous variety of events, and automated counting of these high-density crowds is useful for safety, management, and measuring significance of an event. In this work, we show that the regularly accepted labeling scheme of crowd density maps for trai…
New algorithm learns from partial labels in general scenarios.
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…
Study shows SNN graph Laplacians converge to k-NN graph Laplacians under large scale asymptotics.
Proposes an online metric learning method for multi-label classification.
There is an increasing body of evidence suggesting that exact nearest neighbour search in high-dimensional spaces is affected by the curse of dimensionality at a fundamental level. Does it necessarily mean that the same is true for k nearest neighbours based learning algorithms such as the k-NN classifier? We analyse t…