The problem of learning a sparse model is conceptually interpreted as the process of identifying active features/samples and then optimizing the model over them. Recently introduced safe screening allows us to identify a part of non-active features/samples. So far, safe screening has been individually studied either fo…
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48 results for “Feature Sampling”
Recently introduced generative adversarial network (GAN) has been shown numerous promising results to generate realistic samples. The essential task of GAN is to control the features of samples generated from a random distribution. While the current GAN structures, such as conditional GAN, successfully generate samples…
Sampling strategies significantly affect feature approximations in ELA, impacting classifier accuracy.
problem The impact of sampling strategies on feature approximations in ELA.
method Analysis of feature approximations from different sampling strategies and sample sizes.
result Feature approximations from different sampling strategies do not converge, affecting classifier accuracy.
SSFI identifies key features affecting a single data point's prediction.
problem Understanding feature importance for a single data point.
method Interpretable algorithm using Random Forest's prediction path.
result Relative ranking of features impacting a data point's prediction.
This paper improves random feature sampling using empirical leverage scores.
problem Optimizing the number of features for kernel approximation and supervised learning.
method Uses empirical leverage scores to optimize feature sampling.
result Empirical sampling of random features using leverage scores outperforms vanilla Monte Carlo sampling.
In reinforcement learning, the state of the real world is often represented by feature vectors. However, not all of the features may be pertinent for solving the current task. We propose Feature Selection Explore and Exploit (FS-EE), an algorithm that automatically selects the necessary features while learning a Factor…
A new feature selection method using structural correlation between samples.
problem Feature selection in high-dimensional data overlooks structural correlation information.
method Converts features into graph representations, uses fused lasso for feature selection.
result Demonstrates effectiveness of the proposed approach through experiments.
Quantum algorithm speeds up learning from big data exponentially.
problem Scalable learning from big data with optimized random features.
method Quantum algorithm for sampling optimized random features.
result Exponential speedup in runtime compared to classical algorithms.
A new method for few-sample FS using manifold learning.
problem Few-sample supervised feature selection in high-dimensional spaces.
method Learn feature associations on manifolds, compute composite kernel, and use spectral analysis for FS score.
result Our method outperforms competitors in feature selection and classification accuracy.
Feature selection from wide datasets leads to misleading results.
problem Feature selection in wide datasets with few samples can lead to misleading results.
method Derived sample size requirement for declaring features different, used real datasets to illustrate issues.
result Feature selection from very wide datasets may lead to misleading results.
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.
We introduce single-set spectral sparsification as a deterministic sampling based feature selection technique for regularized least squares classification, which is the classification analogue to ridge regression. The method is unsupervised and gives worst-case guarantees of the generalization power of the classificati…
Optimizes Q-learning for MDPs with linear features, achieving sample efficiency.
problem Finding optimal policies in large-scale MDPs with limited samples.
method Parametric Q-learning with linearly additive features, exploiting monotonicity and noise structure.
result Proves sample optimality with samples for -optimality.
The paper classifies cervical cancer using various techniques and feature selection.
problem Classifying cervical cancer from a dataset with missing values and imbalance.
method Feature selection, over-sampling, under-sampling, dimensionality reduction, and classification techniques.
result Age, first sexual intercourse, number of pregnancies, smokes, hormonal contraceptives, and STDs: genital herpes are the main predictive features with high accuracy.
Detects out-of-distribution and adversarial samples using deep feature distributions.
problem Detecting out-of-distribution and adversarial samples in deep neural networks.
method Modeling deep features with parametric probability distributions and calculating likelihoods at inference.
result Improves detection of out-of-distribution and adversarial samples, up to 12 percentage points in AUPR and AUROC metrics.
This study examines a single attention layer's capabilities using random features.
problem Understanding the learning and generalization of a single multi-head attention layer.
method Random feature setting with large number of heads, frozen query and key matrices, and trainable value matrices.
result Random-feature attention layer can express a broad class of permutation-invariant target functions.
Online feature selection has been an active research area in recent years. We propose a novel diverse online feature selection method based on Determinantal Point Processes (DPP). Our model aims to provide diverse features which can be composed in either a supervised or unsupervised framework. The framework aims to pro…
Sparse support vector machine (SVM) is a popular classification technique that can simultaneously learn a small set of the most interpretable features and identify the support vectors. It has achieved great successes in many real-world applications. However, for large-scale problems involving a huge number of samples a…
A Q-learning algorithm finds Nash equilibrium in two-player stochastic games efficiently.
problem Finding Nash equilibrium in two-player stochastic games with limited samples.
method Feature-based Q-learning algorithm with accelerated techniques for improved sample efficiency.
result The algorithm finds an ε-optimal strategy with sample size linear to the number of features and time/space complexity independent of game dimensions.
AdaTrans: Feature-wise and Sample-wise Adaptive Transfer Learning for High-dimensional Regressionstat.ML
AdaTrans adapts to feature and sample transfer in high-dimensional regression.
problem High-dimensional linear regression with more features than samples.
method F-AdaTrans and S-AdaTrans methods using fused-penalties and adaptive weights.
result AdaTrans achieves convergence rates close to oracle estimators and near-minimax optimal rates.
Losaw improves FI scores by decorrelating features in ML models.
problem Feature correlation distorts feature importance scores in ML models.
method Losaw uses local sample weighting to decorrelate features.
result Losaw consistently improves feature importance scores and prediction accuracy.
DoubleEnsemble improves financial predictions by selecting key features and reweighting samples.
problem Overfitting and instability in financial data analysis.
method Sample reweighting and feature selection using learning trajectory and shuffling.
result DoubleEnsemble outperforms baseline methods in financial prediction tasks.
Spectral dimensionality reduction methods enable linear separations of complex data with high-dimensional features in a reduced space. However, these methods do not always give the desired results due to irregularities or uncertainties of the data. Thus, we consider aggressively modifying the scales of the features to …
New methods ensure feature importance rankings are correct with high probability.
problem Stability issues in feature importance scores due to random sampling.
method Hypothesis testing-based techniques to assess and verify the stability of top-ranked features.
result Ensures the most important features are correct with high-probability guarantees.
Generative Adversarial Networks improve affective speech feature generation.
problem Improving feature representation for emotion recognition.
method Experimented with GAN architectures to generate feature vectors corresponding to emotions.
result GANs generate realistic synthetic samples for emotion recognition.
A new sampling strategy for random Fourier features reduces computation time and improves prediction performance.
problem Efficient generation of random Fourier features for kernel approximation.
method Surrogate leverage weighted sampling guided by kernel alignment, avoiding matrix inversion.
result Time complexity reduced from O(ns^2+s^3) to O(ns^2), comparable or slightly better prediction performance.
New algorithm improves feature selection for streaming data.
problem Traditional OSFS methods assume all data available at runtime, but features and samples stream concurrently.
method Introduces Geometric Online Adaption (GOA) for concurrent streaming of features and samples.
result GOA outperforms SAOLA on various datasets and in the OSFS-SS setting.
Paper develops robust -NN algorithm for few samples.
problem Learning robust classifier from limited samples.
method Distributionally robust formulation of weighted -NN.
result Robust classifier improves generalization with smaller Lipschitz norm.
Graphs from features improve classification accuracy in tasks.
problem Traditional classification tasks can be improved by incorporating relational information.
method Construct geometric graphs from features and use them in Graph Convolutional Networks.
result Graphs derived from features increase classification accuracy and improve class separation.
A new algorithm detects out-of-distribution samples by concentrating them in feature space.
problem Building safe AI systems requires effective out-of-distribution detection.
method The paper proposes a novel algorithm based on the observation that OoD samples concentrate in feature space.
result The algorithm achieves state-of-the-art performance on various OoD detection benchmarks.
Generative model uses random convolutional features to create financial time series.
problem Generating realistic financial time series with limited data and avoiding overfitting.
method Train generators by matching random convolutional features of real and generated time series, using SOCK (SOft Competing Kernels) feature map.
result Generators trained with random SOCK features outperform baselines across various financial datasets.
CoSCA improves unsupervised domain adaptation by better aligning ambiguous target samples.
problem Missing alignment of ambiguous target samples in unsupervised domain adaptation.
method CoSCA explicitly incorporates intra- and inter-class domain discrepancy, estimating label hypotheses and optimizing a contrastive loss with MMD for better global alignment.
result CoSCA outperforms state-of-the-art approaches in producing more discriminative features.
Many machine learning problems, especially multi-modal learning problems, have two sets of distinct features (e.g., image and text features in news story classification, or neuroimaging data and neurocognitive data in cognitive science research). This paper addresses the joint dimensionality reduction of two feature ve…
Sparse Bayesian learning is a state-of-the-art supervised learning algorithm that can choose a subset of relevant samples from the input data and make reliable probabilistic predictions. However, in the presence of high-dimensional data with irrelevant features, traditional sparse Bayesian classifiers suffer from perfo…
We propose a single neural probabilistic model based on variational autoencoder that can be conditioned on an arbitrary subset of observed features and then sample the remaining features in "one shot". The features may be both real-valued and categorical. Training of the model is performed by stochastic variational Bay…
New method scales features for better clustering.
problem Irregular features disrupt classification.
method Spectral clustering with modified feature scales.
result Outperforms existing methods in experiments.
Optimal sparse recovery with decision stumps achieves strong feature selection guarantees.
problem Sparse recovery of active features from high-dimensional data.
method Analysis of single-depth decision trees (decision stumps) for feature selection in linear regression.
result Tight sample performance guarantees for , improving upon previous bounds.
Two statistical tasks are shown to have equivalent sample complexity.
problem Determining if a function depends on only a few variables and identifying those variables.
method Proved statistical equivalence of feature selection and junta testing through sample complexity analysis.
result Brute-force algorithm is sample-optimal for both tasks with optimal sample size.
This work analyzes tree-based methods from a ranking perspective, providing insights and new statistics.
problem Understanding the effectiveness of tree-based methods in finite-sample settings, especially symbolic feature selection.
method Local ranking perspective, finite-sample analysis, oracle bounds, posterior contraction results, concordant divergence statistics.
result New insights and statistics for evaluating symbolic feature mappings.
Feature learning forms the cornerstone for tackling challenging learning problems in domains such as speech, computer vision and natural language processing. In this paper, we consider a novel class of matrix and tensor-valued features, which can be pre-trained using unlabeled samples. We present efficient algorithms f…
Improves performance in various machine learning tasks by reparameterizing subset sampling.
problem Stochastic optimization involving subset sampling is not reparameterizable.
method Continuous relaxation of subset sampling to provide reparameterization gradients.
result Improves performance in instance-wise feature selection, deep stochastic k-nearest neighbors, and parametric t-SNE.
The positive-unlabeled (PU) classification is a common scenario in real-world applications such as healthcare, text classification, and bioinformatics, in which we only observe a few samples labeled as "positive" together with a large volume of "unlabeled" samples that may contain both positive and negative samples. Bu…
Improved preterm prediction using synthetic EHG signals.
problem Prediction bias towards term labor in preterm EHG data.
method Quantifying synthetic samples' effect, optimizing feature weights, and combining activation functions.
result Substantial improvement in prediction precision.
Adapts DR objectives for both sample and feature size reduction.
problem Simultaneously reduce sample and feature sizes.
method Semi-relaxed Gromov-Wasserstein optimal transport.
result OT plan delivers competitive hard clustering.
Deep Feature Screening: Feature Selection for Ultra High-Dimensional Data via Deep Neural Networksstat.ML
DeepFS uses deep neural networks to select significant features in ultra high-dimensional data.
problem Challenges in traditional feature selection methods for high-dimensional, low-sample-size data.
method Two-step nonparametric approach combining deep neural networks and feature screening.
result DeepFS effectively identifies significant features with high precision for ultra high-dimensional data.
Feature learning forms the cornerstone for tackling challenging learning problems in domains such as speech, computer vision and natural language processing. In this paper, we consider a novel class of matrix and tensor-valued features, which can be pre-trained using unlabeled samples. We present efficient algorithms f…
New research shows spurious samples in deep models are not errors but a feature.
problem Whether spurious samples can be eliminated without losing model coverage.
method Experimental study on deep generative models.
result Spurious samples cannot be completely eliminated without losing model ability to generate some test samples.
Rank regression from pairwise comparisons requires many comparisons to accurately learn model parameters.
problem Learning model parameters for rank regression from noisy pairwise comparisons.
method Uniform random pairwise comparisons to estimate model parameters with a given accuracy.
result Learning model parameters requires a number of comparisons proportional to .