Improved object classification using neural networks with known and unknown features.
problem Improving classification accuracy for objects described by both known and unknown features.
method Modernized Informational Neurobayesian Approach with consideration of unknown features.
result The method completely solved the problem of misclassification for queries with combining known and unknown features.
This paper improves conditional multidimensional scaling for incomplete data.
problem Handling missing data in known features for multidimensional scaling.
method Proposes a method to learn low-dimensional configurations with missing known feature values.
result Can learn low-dimensional configurations and impute missing values.
Tree ensemble kernels improve Bayesian optimization for mixed features and constraints.
problem Optimizing over mixed-feature spaces with known constraints.
method Kernel interpretation of tree ensembles as Gaussian Process prior, compatible optimization formulation for acquisition function, integration of known constraints.
result Framework outperforms state-of-the-art methods for mixed-feature spaces and constraints.
In this paper we introduce a new feature selection algorithm to remove the irrelevant or redundant features in the data sets. In this algorithm the importance of a feature is based on its fitting to the Catastrophe model. Akaike information crite- rion value is used for ranking the features in the data set. The propose…
UniFeat is an open-source Java tool for feature selection.
problem Efficient feature selection in various research areas.
method Provides a set of advanced feature selection methods.
result Facilitates rapid development of new feature selection algorithms.
Reconstructing signature features from randomized vector fields in differential equations.
problem Reconstructing signature features from controlled differential equations with random vector fields.
method Using controlled ordinary differential equations driven by continuous bounded variation curves, the study explores the extent to which signature features can be reconstructed from the non-linear flow of these equations.
result The number of signature features that can be reconstructed from the non-linear flow of controlled ordinary differential equations with random vector fields is exponential in the hidden dimension, under certain conditions.
The Hirzebruch χy-genus and Poincare polynomial share some similar features. In this article we investigate two of their similar features simultaneously. Through this process we shall derive several new results as well as reprove and improve some known results.
Improves decision-making by correcting feature selection bias.
problem Cofounding bias in feature selection affects machine learning predictions.
method Proposes a meta-algorithm using a novel adjustment criterion based on causal sufficiency.
result Corrects cofounding bias to improve prediction performance.
New operator reveals unique features of sl(N) link homology.
problem Understanding unique features of sl(N) link homology.
method Constructing an operator on sl(N) link homology with mod N coefficients.
result Structural features of sl(P) link homology are revealed.
The generalized partially linear additive model (GPLAM) is a flexible and interpretable approach to building predictive models. It combines features in an additive manner, allowing each to have either a linear or nonlinear effect on the response. However, the choice of which features to treat as linear or nonlinear is …
A fast method estimates stability of ensemble feature selectors.
problem Improving stability of ensemble feature selectors for better prediction.
method Simulator of a feature selector to estimate stability.
result Reduces computation time for estimating stability.
t-SNE loses important features in data visualization.
problem t-SNE's loss of important features in data visualization.
method Established mathematical framework to understand t-SNE's loss in different scenarios.
result t-SNE loses important features of data in various scenarios.
A feature selection algorithm should ideally satisfy four conditions: reliably extract relevant features; be able to identify non-linear feature interactions; scale linearly with the number of features and dimensions; allow the incorporation of known sparsity structure. In this work we propose a novel feature selection…
Method identifies key features for clustering in high-dimensional data.
problem Understanding hidden patterns in high-dimensional data.
method Unsupervised feature selection based on discriminative power.
result 27 key transcription factors identified, 18 known to define cell states.
Study explores DNNs' reliance on existing vs. new features in physiological signals.
problem Understanding how deep neural networks discover new features in physiological signals.
method Proposes a method to remove hand-engineered features and force DNNs to learn new representations.
result DNNs often rediscover known features, but can also learn new ones.
New method tackles unknown unknowns in machine learning.
problem Unknown classes in training data misperceived as other labels.
method Exploratory machine learning with rejection model, feature exploration, and model cascade.
result The method discovers potentially hidden classes and improves model performance.
GMLP learns feature groups for tabular data without known structure.
problem Deep learning for tabular data with unknown feature interactions.
method Group-wise operations and sparse feature grouping matrix learned through temperature annealing softmax.
result GMLP achieves state-of-the-art classification performance on various datasets.
The paper tackles feature cross search for linear models, providing approximation algorithms and structural results.
problem Maximizing AUC of a linear model trained on feature crosses.
method Submodular optimization, greedy algorithm, and connections to total variation and kernel matrices.
result Simple greedy (1−1/e)-approximation algorithm for maximizing AUC. Learning sparse features can lead to overfitting in neural networks, especially for smooth target functions.
problem Understanding when feature learning in neural networks improves or deteriorates performance.
method Analyzing the effect of feature sparsity on neural network performance and comparing it to lazy training methods.
result Feature learning can lead to overfitting, especially for smooth target functions, due to sparser and less smooth representations.
A conjecturing machine uncovers feature relationships in data.
problem Discovering nonlinear and boolean relationships among features.
method Proposes a conjecturing machine that suggests feature relationships using bounds and expressions.
result Reveals true underlying relationships in datasets.
Our research focuses on analysing human activities according to a known behaviorist scenario, in case of noisy and high dimensional collected data. The data come from the monitoring of patients with dementia diseases by wearable cameras. We define a structural model of video recordings based on a Hidden Markov Model. N…
This paper proposes a novel generic one-class feature learning method based on intra-class splitting. In one-class classification, feature learning is challenging, because only samples of one class are available during training. Hence, state-of-the-art methods require reference multi-class datasets to pretrain feature …
The presence of missing entries in data often creates challenges for pattern recognition algorithms. Traditional algorithms for clustering data assume that all the feature values are known for every data point. We propose a method to cluster data in the presence of missing information. Unlike conventional clustering te…
New method for visualizing high-level concepts in generative models.
problem Challenges in evaluating and visualizing concepts in generative models.
method Introduces a method to compute concept saliency maps for latent representations of known or novel high-level concepts.
result Concept saliency maps highlight input features important for high-level concepts.
SRF improves kernel approximation and GP regression performance.
problem Efficient kernel approximation and Bayesian kernel learning in large-scale regression problems.
method Stein variational gradient descent to generate high-quality random features and approximate spectral measure posteriors.
result SRF outperforms traditional approaches in kernel approximation and GP regression.
FSinR is a comprehensive R package for feature selection.
problem Feature selection in machine learning.
method Filter and wrapper methods, search algorithms.
result Provides a variety of feature selection methods and search algorithms.
This work focuses on support vector machine (SVM) with feature selection. A MILP formulation is proposed for the problem. The choice of suitable features to construct the separating hyperplanes has been modelled in this formulation by including a budget constraint that sets in advance a limit on the number of features …
A new method explains Siamese neural networks using feature comparison and autoencoder.
problem Explaining the decision-making process of Siamese neural networks.
method Feature comparison at embedding level and autoencoder reconstruction.
result The method effectively explains Siamese neural networks using MNIST dataset.
Develops framework to evaluate feature attribution methods.
problem Lack of ground truth for evaluating feature attribution methods.
method Proposes a framework including a dataset and metrics.
result Certain methods produce false positive explanations.
Dilated CNN improves multivariate time series classification.
problem Multivariate time series classification.
method Transformed multivariate time series into image-like style, applied dilated and strided convolutions.
result Automatic features extracted by dilated CNN are as effective as hand-crafted features.
Exponentially fast SMF algorithm for multi-class classification.
problem Learning interpretable features from high-dimensional data.
method Novel framework that 'lifts' SMF as a low-rank matrix estimation problem.
result Provable exponential convergence to global minimizer under mild assumptions.
AMBER method selects features efficiently using autoencoders and model-based elimination.
problem Efficiently selecting relevant features for classification.
method Greedy backward elimination using a ranker model and autoencoders.
result AMBER outperforms other feature selection methods in classification accuracy.
This research aims to develop robust audio spoofing detection methods that work across various spoofing techniques.
problem Detecting audio spoofing attacks in speaker verification systems.
method Examined traditional and machine learned audio features for robust spoofing detection.
result Fused models based on both known and machine learned features achieve comparable performance with an EER of 12.
Study agnostic feature-based dynamic pricing models with linear policies and noisy valuations.
problem Tackles dynamic pricing with unknown noise and no assumptions on data.
method Studies two agnostic models: linear policy and linear noisy valuation, presenting algorithms and regret bounds.
result Demonstrates no-regret learning is possible under weak assumptions, but noisy feedback is not significantly more useful than bandit feedback.
A neural network method determines the latent dimensionality of NMF.
problem Determining the correct number of hidden features (latent dimensionality) in NMF.
method Combining NMFk with an MLP classifier trained on a dataset of matrices with known latent features.
result The MLP classifier in conjunction with NMFk achieves a greater than 95% success rate in determining the correct number of latent features.
In real-world scenarios, different features have different acquisition costs at test-time which necessitates cost-aware methods to optimize the cost and performance trade-off. This paper introduces a novel and scalable approach for cost-aware feature acquisition at test-time. The method incrementally asks for features …
In a regression setting we propose algorithms that reduce the dimensionality of the features while simultaneously maximizing a statistical measure of dependence known as distance correlation between the low-dimensional features and a response variable. This helps in solving the prediction problem with a low-dimensional…
MLS improves feature selection for imbalanced data.
problem Machine learning challenges with imbalanced high-dimensional data.
method Introduces Marginal Laplacian Score (MLS) for better feature selection.
result MLS improves performance on synthetic and public datasets.
We propose a novel algorithm for greedy forward feature selection for regularized least-squares (RLS) regression and classification, also known as the least-squares support vector machine or ridge regression. The algorithm, which we call greedy RLS, starts from the empty feature set, and on each iteration adds the feat…
We show that kernel-based quadrature rules for computing integrals can be seen as a special case of random feature expansions for positive definite kernels, for a particular decomposition that always exists for such kernels. We provide a theoretical analysis of the number of required samples for a given approximation e…
This research simplifies computation of feature attribution methods under certain conditions.
problem Computational complexity of feature attribution methods, especially power indices.
method Identifying conditions for polynomial computation and introducing new indices.
result Conditions for efficient computation of feature attribution methods are identified.
Paper proposes a loss extension for neural networks to improve OSR performance.
problem Open set recognition problem, distinguishing known and unknown classes.
method Introduces a loss function extension to find more discriminative polar representations.
result Significantly improves performance on datasets from different domains.
A new neural network model reduces features in high-dimensional sequential data.
problem Exponential growth in features of truncated signature transform in high-dimensional data.
method Proposes a neural network model inspired by Convolutional Neural Networks to address feature growth.
result Reduces the number of features efficiently in a data-dependent way.
We give two provably accurate feature-selection techniques for the linear SVM. The algorithms run in deterministic and randomized time respectively. Our algorithms can be used in an unsupervised or supervised setting. The supervised approach is based on sampling features from support vectors. We prove that the margin i…
Meta-learning improves feature extraction for few-shot tasks.
problem Understanding why meta-learning models perform better on few-shot classification.
method Developed hypotheses and a regularizer to improve standard training routines.
result Meta-learned models outperform classical training routines in few-shot classification.
Inf-FS selects features by graph paths, ranking them for infinite feature sets.
problem Feature selection in large datasets with relevance and redundancy.
method Graph-based feature selection with infinite paths, evaluating feature subsets using matrix power series and Markov chains.
result Inf-FS outperforms other methods in various feature selection scenarios.
There is no known efficient method for selecting k Gaussian features from n which achieve the lowest Bayesian classification error. We show an example of how greedy algorithms faced with this task are led to give results that are not optimal. This motivates us to propose a more robust approach. We present a Branch and …
Paper tackles class-incremental time series classification with dual-stream feature extraction.
problem Class-incremental continual learning for multivariate time series data.
method Dual-stream feature extraction pipeline combining deep temporal embedding features and statistical features.
result Competitive average accuracy across multiple datasets with low forgetting rates.