A new method LDHA improves fMRI data alignment for cognitive state validation.
problem Accurate functional alignment across different subjects for MVP analysis.
method LDHA incorporates LDA into CCA for supervised fMRI alignment.
result LDHA outperforms other HA methods in MVP analysis.
A new method improves few-shot learning by combining ProtoNet with LFD.
problem Few-shot learning struggles with high variance support sets.
method Combines ProtoNet with Local Fisher Discriminant Analysis.
result Superior classification accuracy on miniImageNet and tieredImageNet.
Study local convergence of GDA for training GANs with kernel-based discriminators.
problem Analyzing the local dynamics of GDA for GANs with kernel-based discriminators.
method Linearization of a non-linear dynamical system, under an isolated points model assumption.
result Showed phase transitions indicating convergence, oscillation, or divergence of GDA.
We present local discriminative Gaussian (LDG) dimensionality reduction, a supervised dimensionality reduction technique for classification. The LDG objective function is an approximation to the leave-one-out training error of a local quadratic discriminant analysis classifier, and thus acts locally to each training po…
We consider the problem of discriminative factor analysis for data that are in general non-Gaussian. A Bayesian model based on the ranks of the data is proposed. We first introduce a new {\em max-margin} version of the rank-likelihood. A discriminative factor model is then developed, integrating the max-margin rank-lik…
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.
A new algorithm improves Wasserstein discriminant analysis for better data classification.
problem Improving data classification in machine learning.
method Bi-level nonlinear eigenvector algorithm (WDA-nepv) for optimal transport and trace ratio optimizations.
result WDA-nepv enhances classification accuracy and scalability.
DM2L tackles missing labels in multi-label learning by modeling local and global rank structures.
problem Missing labels in multi-label learning.
method DM2L imposes local low-rank structures and global high-rank structures on predictions of instances from the same and different labels, respectively.
result DM2L outperforms state-of-the-art methods in multi-label learning with missing labels.
Efficiently estimates sparse LDA across multiple machines.
problem Sparse LDA estimation in high-dimensional data.
method Distributed estimation with debiasing and aggregation.
result Aggregated estimator achieves same statistical rate as centralized method.
Survey of spectral, probabilistic, and deep metric learning methods.
problem Developing effective distance metrics for various machine learning tasks.
method Divided into spectral, probabilistic, and deep approaches, covering various techniques and their applications.
result Comprehensive overview of metric learning methods, including new developments and applications.
Develops DDC to improve clustering with deep neural networks.
problem Low-level indiscriminative representations and lack of pattern relationships in traditional clustering methods.
method Introduces global and local constraints to a deep neural network for adaptive relationship estimation and high-level representation learning.
result DDC outperforms current methods on multiple datasets.
We consider a pseudo-Riemannian metric that changes signature along a smooth curve on a surface, called the discriminant curve. The discriminant curve separates the surface locally into a Riemannian and a Lorentzian domain. We study the local behaviour and properties of geodesics at a point on the discriminant where th…
New framework assesses neural sensitivity to small perturbations.
problem Comparing neural representations' sensitivity to small changes.
method Local decodable information, Fisher information, and projected pullback/Fisher metric.
result Reveals differences in neural sensitivity not captured by activation alignment.
Kernel discriminant analysis uses nonlinear embeddings to improve classification.
problem Limited effectiveness of linear discriminant analysis in capturing nonlinear features.
method Study of nonlinear embeddings in kernel discriminant analysis using polynomial and Gaussian kernels, solving generalized eigenvalue problems.
result Polynomial and Gaussian discriminants capture class differences through population moments and randomized projections.
Local discovery method uncovers direct unfairness in complex systems.
problem Identifying causal pathways of unfairness in complex domains.
method Local discovery for direct discrimination (LD3) method.
result LD3 returns a valid adjustment set (VAS) for assessing unfairness.
Paper proposes distributed sparse multicategory discriminant analysis for classification.
problem Sparse multicategory classification with distributed data.
method Convex formulation, distributed setting, invariant discriminant subspace recovery.
result Distributed sparse multicategory linear discriminant analysis performs as good as centralized version after a few rounds of communications.
Wasserstein Discriminant Analysis (WDA) is a new supervised method that can improve classification of high-dimensional data by computing a suitable linear map onto a lower dimensional subspace. Following the blueprint of classical Linear Discriminant Analysis (LDA), WDA selects the projection matrix that maximizes the …
Locally Valid and Discriminative prediction intervals for deep learning models.
problem Efficient and theoretically sound uncertainty quantification for deep learning models.
method Locally Valid and Discriminative prediction intervals (LVD) using kernel regression.
result Locally Valid and Discriminative prediction intervals (LVD) offer better performance and scalability compared to existing methods.
Proposes a novel linear discriminant analysis for matrix-valued data.
problem Classification of high-dimensional matrix-valued data from imaging studies.
method Efficient nuclear norm penalized regression with low-rank structure.
result Superior performance compared to existing methods in simulations and EEG data.
We propose to investigate test statistics for testing homogeneity in reproducing kernel Hilbert spaces. Asymptotic null distributions under null hypothesis are derived, and consistency against fixed and local alternatives is assessed. Finally, experimental evidence of the performance of the proposed approach on both ar…
Survey of Locally Linear Embedding and its variants.
problem Representing high-dimensional data in a lower-dimensional space while preserving local structure.
method Explains various LLE and variant methods, including kernel LLE, inverse LLE, feature fusion, out-of-sample embedding, incremental LLE, landmark LLE, supervised LLE, robust LLE, fusion with other methods, and weighted LLE.
result Comprehensive overview of LLE and its variants.
Federated survival analysis outperforms local and centralized training, with RSF offering the best balance of discrimination, calibration, and robustness.
problem Survival analysis models require large, diverse cohorts but are limited by privacy regulations and lack of centralized data.
method Federated learning (FL) is used to train shared models without exchanging raw data.
result FL consistently outperforms local training and approaches, and occasionally exceeds centralized performance.
Paper introduces a new probabilistic model for class-specific discriminant analysis.
problem Lack of multi-modal structure consideration in existing class-specific methods.
method Formulates a probabilistic model that incorporates multi-modal negative class structure.
result Proposed model can be directly used for class-specific probabilistic classification.
Paper proposes faster incremental subclass discriminant analysis.
problem Efficiently classify subclasses in incremental data.
method Exact and approximate linear and kernelized solutions.
result Superior training time and accuracy compared to existing methods.
DBNet improves natural language image localization and detection.
problem Natural language-based visual entity localization with limited accuracy.
method Discriminative bimodal neural network (DBNet) trained with extensive negative samples.
result Significantly outperforms previous methods on Visual Genome dataset.
Improved LDA method for better classification and dimensionality reduction.
problem Improving linear discriminant analysis for better classification performance.
method Integrates spectrally-corrected covariance matrix and regularized discriminant analysis.
result SRLDA has a linear classification global optimal solution under spiked model assumption.
Metrics specifying distances between data points can be learned in a discriminative manner or from generative models. In this paper, we show how to unify generative and discriminative learning of metrics via a kernel learning framework. Specifically, we learn local metrics optimized from parametric generative models. T…
PANDA improves linear discriminant analysis in high dimensions with minimal tuning.
problem Linear discriminant analysis in high-dimensional settings.
method PANDA: a tuning-insensitive method for linear discriminant analysis.
result PANDA achieves optimal convergence rates in estimation error and misclassification rate.
New robust discriminant analysis for non-Gaussian data.
problem Classical discriminant analysis struggles with non-Gaussian distributions and contaminated datasets.
method Each data point follows its own ES distribution with arbitrary scale, leading to robust classification.
result Maximum-likelihood estimation and classification are simple, fast, and robust.
Develops a method to interpret deep learning models by identifying key features.
problem Revealing the decision-making process of blackbox models from raw data to predictions.
method Adversarial attacks to localize discriminative features with statistical guarantees.
result Locally identified features are both biologically plausible and statistically significant.
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.
Proposes DCADL for efficient image classification with reduced complexity.
problem Efficiency and discriminative capability in DL methods for image classification.
method Jointly learns a convolutional analysis dictionary and a universal classifier, reducing time complexity.
result Achieves competitive accuracy with reduced computational cost.
Explains Fisher and Kernel Fisher Discriminant Analysis with examples and comparisons.
problem Classifying data with different features and dimensions.
method Projection and reconstruction, scatters analysis, PCA comparison, Fisher forest.
result Equivalence of Fisher and Linear Discriminant Analysis, effectiveness of Fisher forest.
Proposes KMvDA for object recognition from multi-view data.
problem Recognizing objects from different views, even when views are heterogeneous.
method Introduces kernel multi-view discriminant analysis (KMvDA) and uses random Fourier features (RFF) for large-scale learning.
result KMvDA and RFF approximation improve object recognition from multi-view data.
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.
This paper analyzes implicit bias in Deep Linear Discriminant Analysis.
problem The implicit bias of Deep Linear Discriminant Analysis.
method Analyzing gradient flow on a L-layer diagonal linear network.
result Under balanced initialization, the network transforms additive updates into multiplicative updates, conserving the (2/L) quasi-norm.
Paper analyzes null space for class-specific discriminant learning.
problem Improving discriminant learning for class-specific problems.
method Null space analysis for Class-Specific Discriminant Analysis (CSDA).
result Proposed solutions outperform standard CSDA methods.
QFDA combines machine learning and information theory for image classification.
problem Lack of literature on combining machine learning and information theory.
method Quantized Fisher Discriminant Analysis (QFDA) using a cost function for rate-distortion optimization.
result QFDA achieves at least as good classification accuracy as FDA on quantized images.
DFSOS improves sparse discriminant analysis for high-dimensional data.
problem Sparse discriminant analysis in high-dimensional settings with feature selection.
method Deflation-Free Sparse Optimal Scoring (DFSOS) using Bregman iteration and orthogonality-constrained optimization.
result DFSOS achieves comparable or better classification accuracy than deflation-based methods.
A new method converts complex data patterns into simpler vectors for machine learning.
problem Complex persistence diagrams are hard to use in machine learning.
method Persistence codebooks, adapting BoW, VLAD, and FV for PDs.
result Persistence codebooks improve machine learning performance and speed.
Proposes improved classification via transfer learning with regularized linear discriminant analysis.
problem High dimensionality and small sample sizes lead to poor classification performance.
method Regularized random-effects linear discriminant analysis, combining ridge estimates from target and source models.
result Explicit derivation of asymptotic weights and classification error rates in high-dimensional settings.
New method for tensor classification with missing data.
problem Handling incomplete tensor data in high-dimensional classification.
method High-dimensional tensor linear discriminant analysis with TGMM and Tensor LDA-MD.
result Established convergence rates and minimax optimal bounds for misclassification rate.
Explains LDA and QDA for binary and multiple classes.
problem Classification methods in statistical and probabilistic learning.
method Optimization of decision boundaries, estimation of parameters, relation to other methods.
result Equivalence of LDA and Fisher discriminant analysis.
New framework improves classification accuracy using Pillai's trace and ULDA.
problem Traditional LDA's limitations in noise sensitivity and non-invertible matrices.
method Integrates Pillai's trace with ULDA for a unified classifier.
result Effective control of Type I error rates and improved classification accuracy.
New method compresses large sample data for faster discriminant analysis.
problem Large sample sizes in discriminant analysis increase computational burden.
method Proposes a new compression approach for reducing training samples.
result Significant computational gains and superior predictive ability compared to random sub-sampling.
We introduce a new discriminant analysis method (Empirical Discriminant Analysis or EDA) for binary classification in machine learning. Given a dataset of feature vectors, this method defines an empirical feature map transforming the training and test data into new data with components having Gaussian empirical distrib…
The paper examines GANs' forgetting and mode collapse, showing how they relate and impact training.
problem Catastrophic forgetting and mode collapse in GANs during continual learning.
method Investigates the continual learning nature of GANs, analyzing discriminator's output landscapes and convergence.
result Catastrophic forgetting and mode collapse are interrelated and prevent GANs from converging.
A brain computer interface (BCI) is a system which provides direct communication between the mind of a person and the outside world by using only brain activity (EEG). The event-related potential (ERP)-based BCI problem consists of a binary pattern recognition. Linear discriminant analysis (LDA) is widely used to solve…