Deep nearest neighbors outperform self-supervised methods in anomaly detection.
problem Anomaly detection using self-supervised deep methods.
method Simple nearest-neighbor approach on Imagenet pretrained features.
result Nearest-neighbor method outperforms self-supervised methods in accuracy, few shot generalization, training time, and noise robustness.
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.
SDSPCAAN combines supervised and local data structures for better dimensionality reduction.
problem Preserving both global and local data structures for noisy high-dimensional data.
method Supervised discriminative sparse PCA with adaptive neighbors (SDSPCAAN).
result SDSPCAAN improves classification accuracy on high-dimensional datasets.
New approach learns graph representations by contrasting first-order neighbors and graph diffusion views.
problem Learning node and graph level representations from graph data.
method Self-supervised approach using contrastive learning of multi-scale encodings.
result Achieves state-of-the-art performance on 8 out of 8 benchmarks.
This paper introduces PM and PMLP to enhance SSL by considering probability density and cluster assumptions.
problem Insufficient utilization of unlabeled data in SSL.
method Introduces PM to discern similarity and PMLP to consider cluster assumption in label propagation.
result PMLP outperforms other methods in SSL tasks.
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 …
Cellina uses supervised disentanglement to predict cell behavior in tissues.
problem Querying counterfactuals on tissue graphs
method Cellina framework using supervised disentanglement
result Outperforms spatially-informed and non-spatial competitors
BGNN improves GNN by modeling interactions between neighbor nodes.
problem Existing GNN models fail to capture interactions between neighbor nodes, leading to suboptimal performance.
method Proposes a new graph convolution operator that augments the weighted sum with pairwise interactions of neighbor nodes.
result Empirical results show BGNN models outperform traditional GNN models in node classification accuracy.
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…
A new method for semi-supervised classification using graph walks and reinforcement learning.
problem Efficiently classifying nodes in attributed networks with limited labeled data.
method Proposes a reinforcement learning approach to find optimal paths in the graph for classification.
result The method outperforms existing approaches on multiple datasets.
The recently proposed self-ensembling methods have achieved promising results in deep semi-supervised learning, which penalize inconsistent predictions of unlabeled data under different perturbations. However, they only consider adding perturbations to each single data point, while ignoring the connections between data…
The paper proposes a method to learn from neighbor annotations to improve predictions in ambiguous tasks.
problem Ambiguity in structured prediction problems with large output spaces makes exhaustive annotation impractical.
method Proposes an objective to transfer supervision from neighboring examples to improve predictions.
result Consistent improvements over standard maximum likelihood training and baselines in multi-label classification and image-grounded sequence modeling tasks.
HopGAT improves node classification in sparsely labeled graphs by learning from distant neighbors.
problem Classifying nodes in sparsely labeled graphs with limited labeled data.
method Hop-aware supervision mechanism and simulated annealing learning strategy.
result The model achieves high accuracy even with 40% labeled data, reducing performance loss to 3.9%.
Unsupervised classifier performs as well as supervised ones on ImageNet dataset.
problem Achieving performance of supervised learning classifiers without labeled data.
method Incremental shift and rotation operations on selected hyperplanes.
result 6.2% Top 3 probability of error on ImageNet dataset.
Improved graph attention model for noisy graphs.
problem Understanding and improving graph attention in noisy graphs.
method Proposes SuperGAT, a self-supervised graph attention network.
result SuperGAT learns more expressive attention by encoding edges.
This study observes accuracy variations of KNN, SVM, LMNN, and ENN on 11 UCI datasets.
problem To observe the variation of accuracies of KNN, SVM, LMNN, and ENN algorithms on different datasets.
method Implemented KNN, SVM, LMNN, and ENN on 11 UCI datasets and analyzed their accuracy.
result Comparison of algorithm performances on each dataset.
k Nearest Neighbors (kNN) 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…
Paper compares two possibilistic segmentation methods for SAS imagery.
problem Segmenting synthetic aperture sonar images into different seafloor environments.
method Comparison of Possibilistic Fuzzy Local Information C-Means (PFLICM) and Possibilistic K-Nearest Neighbors (PKNN) algorithms.
result PKNN outperforms PFLICM in segmentation performance on SAS images.
Proposes a graph learning framework for clustering and semi-supervised classification.
problem Graph-based clustering and semi-supervised classification techniques have shown impressive performance but lack explicit cluster structure.
method Uses self-expressiveness of samples for global structure and adaptive neighbor approach for local structure. Incorporates rank constraint to ensure optimal performance.
result The proposed method achieves better performance than state-of-the-art methods in clustering and semi-supervised classification.
A new GCN model learns higher-order neighbors without explicit adjacency matrix computation.
problem GCN's performance drops for deeper structures due to limited neighborhood information.
method Assumes higher-order neighbors are similar to first-order neighbors, learns weights through Lasso to minimize feature loss.
result HWGCN achieves state-of-the-art results on various datasets.
Neighbor-encoder learns representations by reconstructing neighbors, outperforming autoencoders.
problem Learning effective representations for various data types.
method Reconstructs neighbors instead of inputs, incorporating domain knowledge through similarity definitions.
result Neighbor-encoder outperforms autoencoders in diverse domains and tasks.
Local Clustering improves semi-supervised learning models.
problem Improving semi-supervised learning models with limited labeled data.
method Local Clustering (LC) method to mitigate confirmation bias in Mean Teacher (MT) model.
result Adding LC loss to MT improves model performance on semi-supervised benchmark datasets.
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.
Improves GCN by sampling neighbors and features for better node representation.
problem GCN's aggregation process treats all neighbors and features equally, leading to suboptimal node representations.
method Introduces a new convolution operation on feature maps constructed from a fixed node bandwidth, then passes to a standard GCN.
result Outperforms competing methods in semi-supervised node classification tasks.
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.
Explicitly or implicitly, most of dimensionality reduction methods need to determine which samples are neighbors and the similarity between the neighbors in the original highdimensional space. The projection matrix is then learned on the assumption that the neighborhood information (e.g., the similarity) is known and f…
Recently popularized graph neural networks achieve the state-of-the-art accuracy on a number of standard benchmark datasets for graph-based semi-supervised learning, improving significantly over existing approaches. These architectures alternate between a propagation layer that aggregates the hidden states of the local…
Framework predicts patient risk progression over time.
problem Predicting how patient risk changes over time.
method Supervised contrastive learning framework with embedding properties.
result Framework outperforms baselines in mortality and cognitive impairment datasets.
A reject option improves partial-label learning's accuracy.
problem Ambiguously labeled data in real-world applications.
method Risk-consistent nearest-neighbor algorithm with a reject option.
result Our method provides the best trade-off between non-rejected predictions' number and accuracy.
This paper compares machine learning algorithms for ecological data.
problem Classifying ecological datasets into subsets with common patterns.
method Applied eight machine learning classification algorithms to ecological data.
result Linear Discriminant Analysis and k-nearest neighbors are the best methods.
Given an n-sample of random vectors (Xi,Yi)1≤i≤n whose joint law is unknown, the long-standing problem of supervised classification aims to \textit{optimally} predict the label Y of a given a new observation X. In this context, the nearest neighbor rule is a popular flexible and intuitive method …
AdaCAD improves semi-supervised classification by focusing on intra-class nodes.
problem Improving semi-supervised classification by addressing inter-class connections in graphs.
method AdaCAD uses a class-attentive diffusion process to adaptively aggregate nodes based on their class similarity.
result AdaCAD significantly outperforms state-of-the-art methods in semi-supervised classification.
New method shows supervised learning can mimic unsupervised learning effectively.
problem The fundamental difference between supervised and unsupervised learning.
method A two-stage procedure where unsupervised model selection is followed by adding outputs without changing parameters.
result Asymptotic out-of-sample risk bounds for various models trained without access to labels.
Paper predicts crimes using historical data and machine learning.
problem Predicting and preventing crime activities.
method Supervised learning with decision tree, k-nearest neighbor, Random Forest, and Adaboost algorithms.
result The system predicts crimes with better accuracy using historical data.
UMAP compared to other methods for dimensionality reduction.
problem Comparing UMAP to other dimensionality reduction techniques.
method Comprehensive evaluation of UMAP and other methods.
result Supervised UMAP performs well for classification but not for regression.
The paper introduces a new loss function to prevent overfitting in semi-supervised graph networks.
problem Overfitting in semi-supervised graph networks trained with cross-entropy loss.
method Proposes an unsupervised manifold smoothness loss to regularize the graph convolutional networks.
result Adding the proposed loss consistently improves performance of graph networks.
Machine learning classifies IoT noise in smart cities.
problem Noise classification in smart cities.
method Mel-frequency cepstral coefficients for audio features, supervised classification (SVM, k-NN), parameter optimization.
result Noise classification accuracy of 85% - 100%.
Centroid-Encoder reduces high-dimensional data for better visualization.
problem Visualizing high-dimensional data efficiently and accurately.
method Centroid-Encoder integrates label information to keep similar objects close in reduced space.
result Centroid-Encoder outperforms other techniques in visualizing high-dimensional data.
New method constructs graphs from data efficiently, suitable for large datasets.
problem Memory and runtime limitations of traditional TMFG for large datasets.
method Uses k-Nearest Neighbors Graphs and memory management for scalable graph construction.
result Provides a parsimonious way to construct graphs for learning tasks.
LC-GNN improves GNNs for node classification by incorporating label consistency.
problem Limited performance of GNNs due to label consistency assumption not always holding.
method LC-GNN uses node pairs with the same label but unconnected to expand GNN's receptive field.
result LC-GNN outperforms traditional GNNs in semi-supervised node classification.
Missing data imputation can help improve the performance of prediction models in situations where missing data hide useful information. This paper compares methods for imputing missing categorical data for supervised classification tasks. We experiment on two machine learning benchmark datasets with missing categorical…
In the framework of supervised classification (discrimination) for functional data, it is shown that the optimal classification rule can be explicitly obtained for a class of Gaussian processes with "triangular" covariance functions. This explicit knowledge has two practical consequences. First, the consistency of the …
Boost GNNs for node classification by incorporating label dependencies.
problem Current GNNs lack expressiveness and fail to capture label dependencies.
method Proposes a collective learning framework combining collective classification and self-supervised learning.
result Consistent, significant improvement in node classification accuracy across various GNNs.
Extends RF proximities to all supervised distance-based machine learning contexts.
problem Limited utility of RF proximities in various machine learning tasks.
method Introduces generalized Proximity Forest (PF) model and variant for regression.
result Demonstrates unique advantages over RF and k-nearest neighbors models.
Unified meta-learning framework from supervised learning.
problem Difficulty in comparing and evaluating meta-learning approaches.
method Treating meta-learning as supervised learning, reducing algorithms to supervised learning instances.
result Unified framework and improved model performance on few-shot learning.
We introduce nonlinear higher-order label spreading for semi-supervised learning.
problem Efficient semi-supervised learning on graphs with complex label spreading.
method We add nonlinearity to label spreading through higher-order graph structures, proving convergence and demonstrating efficiency on various datasets.
result Our nonlinear higher-order label spreading algorithm converges to the global solution and performs favorably compared to classical methods.
CuBAS selects informative data points based on curvature for better classification.
problem Lack of efficient sampling strategies for maximizing dataset informativeness.
method Information-geometric framework using curvature scores to select labeled data.
result Consistent and statistically significant improvements over random and uncertainty-based sampling.
NN methods perform well in anomaly detection, especially in high dimensions.
problem Improving anomaly detection methods for high-dimensional data.
method Extensive simulations and theoretical analysis of NN methods for anomaly detection.
result NN methods outperform other state-of-the-art algorithms in anomaly detection.