New algorithms mix spatial and spectral data to improve unmixing of hyperspectral images.
problem Improving spectral unmixing in hyperspectral images.
method Introduced a novel convex mixed penalty term combining ℓ1 and nuclear norm regularization, applied to a sliding window of the image. result Demonstrated enhanced estimation results for abundance matrix in hyperspectral images.
CIBP models feature abundance in latent feature models.
problem Modeling feature abundance in latent feature models.
method Proposes a new Bayesian nonparametric prior, the CIBP, for latent feature models.
result The expected number of features is bounded even as the number of objects increases.
Boosting GAMLSS with stability selection for eider abundance estimation.
problem Estimating abundance with spatio-temporal structures and excess zeros.
method Noncyclical boosting algorithm with stability selection for variable selection.
result Stability selection reduces complexity and provides sparse, stable predictors.
Unified framework for sparse logistic regression with nonconvex regularization.
problem Sparse logistic regression with nonconvex regularization.
method Unified framework, line search criteria for nonconvex terms.
result Effective classification and feature selection at lower computational cost.
The non-vanishing conjecture implies the abundance conjecture in certain cases.
problem Abundance conjecture in algebraic geometry.
method Proof of the abundance conjecture under specific conditions.
result The abundance conjecture holds in dimensions ≤ 5 when κ ≥ 0 and ν ≤ 1.
GLIMPS tackles abundant outlier detection in matched subspace detection.
problem Detecting matched subspaces in high-dimensional data with a high proportion of outliers.
method Two-stage approach combining greedy algorithm and mixed integer programming.
result GLIMPS can tolerate over 80% outliers, significantly outperforming state-of-the-art methods.
A new stochastic solver improves Convolutional Sparse Coding efficiency.
problem Efficiency and sparsity in Convolutional Sparse Coding.
method Randomized subsampling strategy in spatial domain for online learning.
result Improved execution time with no loss in learning quality.
PHIBP predicts infectious disease outbreaks in sparse data regions.
problem Predicting outbreaks in regions with no historical data.
method Poisson Hierarchical Indian Buffet Process (PHIBP) framework.
result PHIBP provides accurate outbreak predictions and meaningful insights in sparse data settings.
Paper presents robust hyperspectral unmixing using correntropy maximization.
problem Robust unmixing of hyperspectral images with noisy bands.
method Maximizes correntropy criterion using ADMM for non-negativity and sparsity constraints.
result ADMM-based correntropy maximization is robust to noisy bands.
Predictive analytics uses proxy data when true data is scarce, but this can lead to errors.
problem Bias in using proxy data for predictive analytics.
method Proposes a two-step estimator using high-dimensional statistics to combine proxy and true data.
result Achieves better predictive accuracy with less true data.
BioHash improves similarity search performance using sparse high-dimensional hash codes.
problem Improving similarity search performance in high-dimensional data.
method BioHash produces sparse high-dimensional hash codes through a data-driven approach based on synaptic plasticity.
result BioHash outperforms previous hashing methods in various similarity search tasks.
Paper introduces MTGP for engineering tasks with sparse data.
problem Challenges of data sparsity and varying task correlations in engineering.
method Multi-Task Gaussian Processes (MTGP) framework.
result Improves predictive performance and reduces computational costs.
Study of superintegrable systems linked to affine hypersurfaces.
problem Understanding superintegrable systems through geometric structures.
method Established a correspondence between superintegrable systems and affine hypersurfaces, defining conformal equivalence.
result Identified conformal classes of abundant manifolds with abundant hypersurface immersions.
SDSPCA improves PCA for disease diagnosis using sparse components and discriminative information.
problem Class ambiguity and low interpretability in traditional PCA.
method Incorporates discriminative information and sparsity into PCA, focusing on sparse components.
result SDSPCA outperforms other methods in gene selection and tumor classification on multi-view biological data.
Sparse representations using learned dictionaries are being increasingly used with success in several data processing and machine learning applications. The availability of abundant training data necessitates the development of efficient, robust and provably good dictionary learning algorithms. Algorithmic stability an…
A new image interpolation model using sparse representation and nonlocal linear regression.
problem Image interpolation without blurring and noise.
method Sparse representation, nonlocal self-similarity, nonlocal linear regression, adaptive sub-dictionary learning, weighted encoding.
result Our method outperforms state-of-the-art methods in quantitative measures and visual quality.
The paper proves conditions for the Abundance conjecture in minimal projective klt pairs.
problem Proving the Abundance conjecture for minimal klt pairs with non-zero canonical bundle.
method Analyzing asymptotic behavior of multiplier ideals and properties of supercanonical currents.
result Supercanonical currents are central to proving the Abundance conjecture.
A semi-supervised framework using stochastic interpolation and latent representations.
problem Challenges in conditional generative modeling with scarce labeled data.
method Combines conditional stochastic interpolation with low-dimensional latent representations.
result Significantly improves sample complexity and achieves faster convergence rate.
New algorithm optimizes AUC for sparse high-dimensional data in online learning.
problem Optimizing AUC for imbalanced classification with high-dimensional sparse data.
method Proposes extsc{FTRL-AUC} algorithm with reduced per-iteration cost and sparsity.
result Significantly improves AUC scores and model sparsity in real-world datasets.
This paper presents an unsupervised algorithm for nonlinear unmixing of hyperspectral images. The proposed model assumes that the pixel reflectances result from a nonlinear function of the abundance vectors associated with the pure spectral components. We assume that the spectral signatures of the pure components and t…
Bayesian framework predicts aerodynamic uncertainty from sparse measurements.
problem Calibrating aerodynamic models with sparse and uncertain measurements.
method Bayesian latent Gaussian process for surrogate model calibration.
result Calibrated surrogate model accurately predicts aerodynamic uncertainty.
Developed a multiway classification method for sparse data.
problem Classification of multiway arrays with sparsity.
method Extended Distance Weighted Discrimination (DWD) to multiway context, accounting for sparsity.
result Improves classification accuracy in multiway structured data.
Study shows torsion grows subexponentially in book of I-bundles but can grow exponentially in non-regular covers.
problem Growth rates of torsion in book of I-bundles.
method Analysis of torsion in homology of book of I-bundles using finite-sheeted covers.
result Torsion growth rates differ between regular and non-regular finite-sheeted covers.
The paper proves conditions for minimal compact Kähler manifolds with vanishing second Chern class.
problem Conditions for minimal compact Kähler manifolds with vanishing second Chern class.
method Study of the abundance conjecture and associated Iitaka fibrations.
result For a minimal compact Kähler manifold, the second Chern class vanishes if and only if the cotangent bundle is nef and the canonical bundle has numerical dimension 0 or 1.
META2 improves taxonomic classification and abundance estimation in metagenomics with deep learning and memory efficiency.
problem Memory constraints and inefficiencies in taxonomic classification and abundance estimation for metagenomics.
method Developed a novel memory-efficient read classification technique combining deep learning and locality-sensitive hashing, and formulated abundance estimation as a Multiple Instance Learning problem.
result Our approach outperforms conventional methods in both single-read taxonomic classification and abundance estimation, especially when memory is limited.
We formulate and prove that there are "abundant" in nilpotent orbits in real semisimple Lie algebras, in the following sense. If S denotes the collection of hyperbolic elements corresponding the weighted Dynkin diagrams coming from nilpotent orbits, then S span the maximally expected space, namely, the (-1)-eigenspace …
Paper analyzes weak-to-strong generalization in CNNs, identifying data-scarce and data-abundant regimes.
problem Weak-to-strong generalization in CNNs trained on weak models.
method Formal analysis of gradient descent dynamics in data-scarce and data-abundant regimes.
result Identifies two regimes and distinct mechanisms of generalization in each.
Study on geodesics on manifold groups, measuring abundance or scarcity.
problem Determine if word metrics on manifold groups have finite or infinite diameter.
method Investigate word metrics on fundamental groups of manifolds with closed geodesic generating sets.
result Abundance or scarcity of closed geodesics can be measured by the finiteness or infiniteness of word metrics' diameter.
A collaborative convex framework for factoring a data matrix X into a non-negative product AS, with a sparse coefficient matrix S, is proposed. We restrict the columns of the dictionary matrix A to coincide with certain columns of the data matrix X, thereby guaranteeing a physically meaningful dictionary and …
Paper proposes a single task optimization for endmembers' number estimation and unmixing.
problem Endmembers' number estimation and unmixing in hyperspectral images.
method Low-rank and sparse nonnegative matrix factorization with alternating proximal algorithm.
result Effectiveness of the proposed approach verified by experiments.
Superintegrable systems on curved manifolds found to have Hessian structures.
problem Characterizing superintegrable systems on curved manifolds.
method Identifying and computing Hessian coordinates for superintegrable systems.
result Examples of superintegrable systems in 2D and 3D have natural Hessian coordinates.
The paper compares different deep architectures for feature learning from EHRs.
problem Extracting meaningful insights from high-dimensional, sparse clinical data.
method Uses stacked sparse autoencoders, deep belief networks, adversarial autoencoders, and variational autoencoders for feature representation.
result Stacked sparse autoencoders perform better for small data sets, while variational autoencoders outperform for large data sets.
PHIBP models complex microbiome data with shared parameters.
problem Complex, sparse count data in microbiome analysis.
method Bayesian nonparametric framework with shared species parameters.
result Flexible multivariate count model with tractable inference.
New product structures encode superintegrable Hamiltonian systems in Euclidean spaces.
problem Encoding superintegrable Hamiltonian systems using product structures.
method Introducing commutative and associative product structures on Euclidean spaces of dimension at least three, satisfying specific conditions.
result All abundant superintegrable Hamiltonian systems on Euclidean space of dimension at least three arise from these product structures.
This paper compares different deep learning techniques for feature learning from EHRs.
problem Extracting meaningful insights from high-dimensional, sparse clinical data.
method Uses stacked sparse autoencoders, deep belief networks, adversarial autoencoders, and variational autoencoders for feature representation.
result Variational autoencoders outperform other methods for large data sets, while stacked sparse autoencoders are superior for small data sets.
New hypergraph method improves scRNA-seq clustering.
problem Loss of higher-order information and overestimation in coexpression networks.
method Conceptualizing scRNA-seq data as hypergraphs and proposing novel clustering methods.
result Proposed methods outperform existing methods on simulated and real datasets.
New method stabilizes model selection with theoretical guarantees.
problem Stability of model selection methods in the face of noisy or incomplete data.
method Combines bagging with an 'inflated' argmax operation to select a stable set of models.
result Stable model selection with high probability of overlap after removing any data point.
The study finds abundant normal generators for mapping class groups.
problem Understanding normal generation in mapping class groups.
method Analyzing restrictions on invariant subsurfaces and Teichmüller spaces.
result Reducible mapping classes can normally generate mapping class groups based on their asymptotic translation lengths.
DWTS uses observational data to improve clinical trial efficiency.
problem Lack of definitive conclusions from randomized clinical trials due to insufficient patient cohorts and confounding biases.
method DWTS combines observational data with randomized clinical trials using Doubly Debiased LASSO (DDL) to identify reliable covariates.
result DWTS reduces cumulative regret in clinical trials compared to standard methods.
Gene expression levels in a population vary extensively across tissues. Such heterogeneity is caused by genetic variability and environmental factors, and is expected to be linked to disease development. The abundance of experimental data now enables the identification of features of gene expression profiles that are s…
New method uses neural networks to infer dark matter subhalo abundance from stellar streams.
problem Constrain warm dark matter mass using stellar streams.
method Amortized Approximate Likelihood Ratios (AALR) for likelihood-free Bayesian inference.
result Demonstrates effectiveness of new method for estimating dark matter subhalo abundance.
Optimal transport aligns rotated linear regression models across domains.
problem Aligning rotated linear regression models across domains with differing statistical properties.
method Combines K-means clustering, OT, and SVD to estimate rotation angle and adapt regression model.
result Optimal transport map recovers underlying rotation in R2. Bayesian method finds consistent patterns in multiple data sources.
problem Finding consistent patterns in multiple data sources.
method Sparse group factor analysis for joint biclustering of multiple data sources.
result The method reliably infers bi-clusters from heterogeneous data sources.
EPICSCORE improves conformal scores by explicitly accounting for epistemic uncertainty.
problem Overconfident predictions in data-sparse regions due to lack of epistemic uncertainty.
method Model-agnostic approach using Bayesian techniques like Gaussian Processes, Dropout, and Regression Trees.
result Enhanced predictive intervals that adaptively expand in sparse data regions and maintain compact intervals in abundant data.
This work examines how low-rank weights improve adversarial robustness in neural networks.
problem Improving adversarial robustness in neural networks.
method The study investigates the impact of low-rank structure on adversarial robustness through compression measures.
result Promoting low-rank structure in weight matrices enhances adversarial robustness in neural networks.
Study semi-supervised learning with noisy proxy covariates, deriving bounds and showing gains.
problem Learning from noisy proxy covariates with scarce labels.
method Two-stage estimator learning kernel eigenfeatures from all proxy covariates and fitting a ridge predictor on labeled data.
result Finite sample bounds show fast labeled sample rates and consistent gains over supervised and semi-supervised baselines.
New optimizer G-AdaGrad improves upon AdaGrad for non-convex machine learning problems.
problem Solving non-convex machine learning problems efficiently.
method Proposes a new optimizer G-AdaGrad and analyzes its convergence using state-space models.
result Empirical results show G-AdaGrad performs better than AdaGrad and Adam.
Method maps imperfect simulations to observed stellar spectra using unsupervised domain adaptation.
problem Mapping from large sets of imperfect simulations and observational data.
method Adversarial autoencoders, cycle-consistency constraint, and generative surrogate physics emulator network.
result Reconstructed spectra quality and discovery of new spectral features.