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…
On-device research index
arXiv research
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.
169,341 papers · 148 categories
Trend · papers per month
48 results for “discriminative feature extraction”
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…
Discriminators can be good feature extractors despite their task focus.
problem Discriminators' features are often considered useless for downstream tasks.
method Theoretical analysis and feature space examination to understand discriminator's role.
result Discriminator features are robust and can prevent mode collapse, making them useful for transfer learning.
Improves GAN performance without changing network architecture.
problem Discriminator fails to guide generator effectively.
method Cascading Rejection (CR) module for discriminator.
result Significantly improves GAN performance in terms of FID.
This paper reviews methods for feature selection and extraction in pattern analysis.
problem Complex raw data require feature selection or extraction for better discrimination or representation.
method Reviews different methods of feature selection and extraction.
result Compares various methods of feature selection and extraction.
Global feature model improves early AD diagnosis accuracy.
problem Early diagnosis of mild cognitive impairment (MCI) using structural MRI images.
method Gaussian discriminant analysis (GDA) with dual high-dimensional decision spaces for global feature extraction.
result Achieved F1 score of 91.06% for MCI vs. CN group.
Paper enhances haptic signals distinguishability with boosted technique.
problem Lack of large datasets in haptics domain limits feature extraction.
method General framework for haptic signal analysis, using spectral features and boosted embedding.
result Framework needs less training data and outperforms state-of-the-art.
GANs can be used to extract Fisher vectors for unsupervised feature learning.
problem Unsupervised feature extraction for classification and similarity tasks.
method Derive Fisher Information and Fisher Vectors from GANs' density model.
result GAN-induced Fisher Vectors perform competitively in unsupervised feature extraction.
Adaptive density discrimination improves metric learning for better classification and feature extraction.
problem Challenges in distance metric learning, especially in performance and feature extraction compared to modern classification algorithms.
method Explicitly models class distributions, adapts similarity assessment, and penalizes class overlap.
result Achieves state-of-the-art classification results, surpassing softmax classifier and triplet loss.
Study examines how familiarity of images affects EEG patterns.
problem Understanding brain responses to images of varying familiarity.
method Recorded EEG of 21 subjects viewing images of varying familiarity levels. Features extracted from EEG data, including spatial-statistical, wavelet, frequency, and harmonic parameters. SVM used for classification.
result Best classification rates in pre-frontal and frontal regions, with wavelet, frequency, and harmonic features being most discriminative.
End-to-end system improves speaker verification using attention mechanism.
problem Improving text-dependent speaker verification accuracy.
method Speaker discriminative CNNs extract features, attention mechanism combines them, end-to-end training optimizes system.
result The proposed system achieves better performance on Windows 10 speaker verification task.
Enhances model's ability to distinguish target domain by adding a new class.
problem Improving unsupervised domain adaptation models' discriminative power.
method Training model on data from a new class generated by GAN, repositioning current class data.
result Achieves state-of-the-art performance in various unsupervised domain adaptation scenarios.
Representing examples in a way that is compatible with the underlying classifier can greatly enhance the performance of a learning system. In this paper we investigate scalable techniques for inducing discriminative features by taking advantage of simple second order structure in the data. We focus on multiclass classi…
MetFA aligns source and target domains for cross-device image classification.
problem Learning discriminative class boundaries across different domains.
method Distance metric guided feature alignment (MetFA) for domain-invariant and discriminative feature extraction.
result MetFA outperforms state-of-the-art methods in cross-device image classification.
Measures CNN features' discriminative power for various datasets.
problem Improving feature representation transfer in CNNs.
method Statistical analysis of CNN features across 11 datasets.
result Low and middle level features behave differently under certain conditions.
New TCL principle finds optimal time segment discrimination for unsupervised feature learning.
problem Identify and learn features from time series data without supervision.
method Introduces time-contrastive learning (TCL) and shows its relation to nonlinear ICA.
result TCL provides the first identifiable nonlinear ICA model up to point-wise transformations.
VPFD uses vocoder features for adversarial training in VC.
problem Adversarial training on waveform data is time-consuming and memory-intensive.
method VPFD employs vocoder features for adversarial training.
result VPFD achieves VC performance comparable to waveform discriminators with reduced training time and memory.
Proposes RLAR for efficient labeled data classification with robust margin and manifold structure.
problem Clear margin representation and data manifold structure difficulty in linear discriminant methods.
method Introduces retargeted regression for adaptive margin learning and locality-aware strategy for compact data manifold.
result RLAR outperforms state-of-the-art approaches in UCI and benchmark data sets.
Improves GANs by enforcing diverse feature learning.
problem GANs can collapse to a single configuration and be unstable.
method Enforces diverse feature learning by penalizing correlated features.
result Enforces diverse features, stabilizes training, and improves image synthesis.
Unequal Covariance Awareness for Fisher Discriminant Analysis and Its Variants in Classificationcs.LG
A new classification rule for FDA improves classification performance by accounting for unequal covariance matrices.
problem Unequal covariance matrices in practical situations affect the performance of FDA and its variants.
method Proposes a novel classification rule for FDA that accounts for unequal covariance matrices, applicable to many FDA variants.
result The new classification rule improves classification performance compared to original FDA and variants.
Develops a novel approach for discriminative data analysis.
problem Challenges of analyzing multiple datasets using PCA.
method Solves a generalized eigenvalue problem by performing SVD once.
result Establishes optimality in the least-squares sense.
Improved EEG event classification using differential energy.
problem Automatic classification of EEG signals from time frequency representations.
method Comparison of feature extraction techniques, including differential energy and derivatives.
result 24% absolute reduction in error rate, improved discrimination between signal events and noise.
A genetic algorithm-based method extracts features for epilepsy EEG classification.
problem Classifying epileptic EEG signals for accurate diagnosis.
method GAFDS method using genetic algorithm for frequency-domain feature search and optimization.
result GAFDS features improve classification accuracy compared to nonlinear features.
A new framework extracts informative features from multiple data types.
problem Finding maximally correlated feature representations from multimodal data.
method Soft-HGR framework, which relaxes whitening constraints while preserving feature geometry.
result Soft-HGR learns more informative feature mappings and is more efficient to optimize.
New discriminant analysis using GDS projection improves face recognition.
problem Improving face recognition accuracy with limited data.
method GDS projection onto generalized difference subspace, simplified Fisher criterion, normalization.
result GDS projection and gFDA are equivalent, inheriting FDA's discriminant ability.
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.
Adversarial model improves implicit relation classification without explicit connectives.
problem Lack of explicit connectives makes implicit discourse relation classification challenging.
method Feature imitation framework with adversarial training.
result State-of-the-art performance on PDTB benchmark.
Enhances SFA for better regression and classification tasks.
problem Improving regression and classification accuracy with graph-based SFA.
method Exact Label Learning (ELL) method to create graphs coding desired labels.
result Higher classification rate with limited output features.
DPFRL uses particle filters for decision making with complex visual observations.
problem Decision making with partial complex visual observations.
method Discriminative Particle Filter Reinforcement Learning (DPFRL) with a differentiable particle filter in the neural network policy.
result DPFRL outperforms state-of-the-art POMDP RL models in complex visual observation tasks.
Manifold regularization improves DNN performance in speech recognition.
problem Training deep neural networks for speech recognition is challenging.
method Manifold learning is used to regularize DNNs, preserving speech feature relationships.
result Manifold regularized DNNs reduce word error rate by up to 37%.
A new PCA method for analyzing multiple datasets.
problem Analyzing multiple datasets for discriminative features.
method Discriminative PCA (dPCA) for feature extraction.
result dPCA optimally recovers target data components.
New method improves OOD detection by integrating diffusion models into discriminator models.
problem Overconfidence in discriminator models leads to poor OOD detection.
method Integrates diffusion models into discriminator and generation models to mitigate overconfidence.
result Demonstrates significant improvement in AUROC scores for challenging datasets.
MPRI learns multiscale features for HSI classification.
problem Hyperspectral image classification with limited training samples.
method MPRI combines PRI and regularized LDA for iterative feature learning.
result MPRI outperforms state-of-the-art methods in HSI classification.
Proposes a framework for extracting consistent physiological features across users.
problem Variability of biosignals across different users and tasks.
method Adversarial feature extractor for disentangled universal representations.
result Up to 8.8% improvement in average accuracy of classification.
Quantum machine learning model for binary classification.
problem Efficiency in high-dimensional binary classification tasks.
method Quantum-classical hybrid algorithm and quantum computer for inference.
result Quantum discriminator achieves 99% accuracy on Iris dataset.
A method extracts binary features directly from CS measurements for compressive image classification.
problem Efficiently classify images using compressive sensing without reconstruction.
method DCT-based approach for binary feature extraction from CS measurements, feature fusion with CNN features.
result Fused features outperform state-of-the-art methods in image classification.
Method extracts features from signals for classification with explainability.
problem Lack of interpretability in signal classification models.
method Combining scattering transform and multiclass logistic regression with zeroth-order optimization.
result Uncovered the meaning of scattering transform coefficients.
Proposes a new layer for efficient 3D shape discrimination.
problem Irregular structure and redundancy in 3D point clouds hinder efficient inter-class discrimination.
method Integrates Blended Convolution and Synthesis layer that projects and synthesizes 3D point clouds, followed by 3D convolution in the unit ball.
result End-to-end architecture achieves compelling results on 3D shape recognition and retrieval.
Paper introduces new loss functions for Siamese networks using FDA.
problem Training Siamese networks with improved loss functions.
method Proposes Fisher Discriminant Triplet (FDT) and Fisher Discriminant Contrastive (FDC) loss functions based on FDA.
result Shows effectiveness of FDT and FDC on MNIST and histopathology datasets.
KM-GAN generates images without labels using K-Means and GANs.
problem Reducing label dependence in GANs for efficient image generation.
method KM-GAN integrates K-Means clustering into GANs' framework for feature extraction and updates.
result KM-GAN generates high-quality images comparable to conditional GANs.
Improves unsupervised feature learning with an exclusivity concept.
problem Overfitting in AE-based unsupervised feature learning.
method Integrates exclusivity concept to enhance AE's latent feature representation.
result Significant improvement in performance compared to other methods.
Human Action Recognition Using Deep Multilevel Multimodal (M2) Fusion of Depth and Inertial Sensorscs.LG
New deep fusion methods improve human action recognition using depth and inertial sensor data.
problem Existing multimodal HAR frameworks lack mid-level feature fusion.
method Proposes three deep multilevel multimodal fusion frameworks, transforming depth and inertial sensor data into images and using convolution with Prewitt filter to create modality within modality.
result Supremacy of proposed fusion frameworks over existing methods on three publicly available datasets.
Improves speaker verification for variable-duration utterances using a feature pyramid module.
problem Improving robustness for variable-duration utterances in speaker verification.
method Integrates a feature pyramid module into multi-scale aggregation to enhance speaker-discriminative information from multiple layers.
result Improves performance for both short and long utterances compared to state-of-the-art approaches.
Machine learning classifies Parkinson's Disease stages from walker sensors data.
problem Limited cost-effective methods for quantitatively assessing Parkinson's Disease stages.
method Machine learning applied to walker-mounted sensors data, feature selection methods compared.
result Feature selection method using ANOVA provides similar accuracy to full feature set and is clinically interpretable.
ELICA helps analysts extract relevant information during elicitation meetings.
problem Challenges in eliciting requirements due to analyst's lack of domain knowledge.
method ELICA uses a novel information extraction algorithm combining WFSTs and SVMs, presented in an interactive GUI.
result ELICA effectively extracts relevant information in real-time and facilitates interactive and dynamic process.
This paper introduces a new method for SAR imagery region discrimination using geodesic distances.
problem Region discrimination in monopolarized SAR imagery.
method Geodesic distance between models.
result Advantages of using geodesic distance over stochastic distances.
Supervised Dimensionality Reduction Revisited: Why LDA on Frozen CNN Features Deserves a Second Lookcs.LG
LDA improves image classification accuracy with fewer features.
problem Fine-grained image classification with pretrained features.
method Supervised dimensionality reduction with LDA before linear probing.
result LDA improves accuracy over full features in 11 out of 12 configurations.
GA optimizes EEG feature selection for BCI systems, improving classification accuracy.
problem Finding optimal EEG features for accurate classification in BCI systems.
method Genetic Algorithm (GA) for feature selection and classifier optimization.
result Katz fractal feature with LDA yields highest fitness value.