Tootfinder

Navigating High-Dimensional Backstage: A Guide for Exploring Literature for the Reliable Use of Dimensionality Reduction
Hyeon Jeon, Hyunwook Lee, Yun-Hsin Kuo, Taehyun Yang, Daniel Archambault, Sungahn Ko, Takanori Fujiwara, Kwan-Liu Ma, Jinwook Seo
https://arxiv.org/abs/2506.14820

Navigating High-Dimensional Backstage: A Guide for Exploring Literature for the Reliable Use of Dimensionality Reduction
Visual analytics using dimensionality reduction (DR) can easily be unreliable for various reasons, e.g., inherent distortions in representing the original data. The literature has thus proposed a wide range of methodologies to make DR-based visual analytics reliable. However, the diversity and extensiveness of the literature can leave novice analysts and researchers uncertain about where to begin and proceed. To address this problem, we propose a guide for reading papers for reliable visual ana…

How does Labeling Error Impact Contrastive Learning? A Perspective from Data Dimensionality Reduction
Jun Chen, Hong Chen, Yonghua Yu, Yiming Ying
https://arxiv.org/abs/2507.11161

How does Labeling Error Impact Contrastive Learning? A Perspective from Data Dimensionality Reduction
In recent years, contrastive learning has achieved state-of-the-art performance in the territory of self-supervised representation learning. Many previous works have attempted to provide the theoretical understanding underlying the success of contrastive learning. Almost all of them rely on a default assumption, i.e., the label consistency assumption, which may not hold in practice (the probability of failure is called labeling error) due to the strength and randomness of common augmentation st…

Dataset-Adaptive Dimensionality Reduction
Hyeon Jeon, Jeongin Park, Soohyun Lee, Dae Hyun Kim, Sungbok Shin, Jinwook Seo
https://arxiv.org/abs/2507.11984 h…

Dataset-Adaptive Dimensionality Reduction
Selecting the appropriate dimensionality reduction (DR) technique and determining its optimal hyperparameter settings that maximize the accuracy of the output projections typically involves extensive trial and error, often resulting in unnecessary computational overhead. To address this challenge, we propose a dataset-adaptive approach to DR optimization guided by structural complexity metrics. These metrics quantify the intrinsic complexity of a dataset, predicting whether higher-dimensional s…

Demonstrating Superresolution in Radar Range Estimation Using a Denoising Autoencoder
Robert Czupryniak, Abhishek Chakraborty, Andrew N. Jordan, John C. Howell
https://arxiv.org/abs/2506.14906

Demonstrating Superresolution in Radar Range Estimation Using a Denoising Autoencoder
We apply machine learning methods to demonstrate range superresolution in remote sensing radar detection. Specifically, we implement a denoising autoencoder to estimate the distance between two equal intensity scatterers in the subwavelength regime. The machine learning models are trained on waveforms subject to a bandlimit constraint such that ranges much smaller than the inverse bandlimit are optimized in their precision. The autoencoder achieves effective dimensionality reduction, with the b…

Randomized Dimensionality Reduction for Euclidean Maximization and Diversity Measures
Jie Gao, Rajesh Jayaram, Benedikt Kolbe, Shay Sapir, Chris Schwiegelshohn, Sandeep Silwal, Erik Waingarten
https://arxiv.org/abs/2506.00165

Randomized Dimensionality Reduction for Euclidean Maximization and Diversity Measures
Randomized dimensionality reduction is a widely-used algorithmic technique for speeding up large-scale Euclidean optimization problems. In this paper, we study dimension reduction for a variety of maximization problems, including max-matching, max-spanning tree, max TSP, as well as various measures for dataset diversity. For these problems, we show that the effect of dimension reduction is intimately tied to the \emph{doubling dimension} $λ_X$ of the underlying dataset $X$ -- a quantity measur…

Enabling stratified sampling in high dimensions via nonlinear dimensionality reduction
Gianluca Geraci, Daniele E. Schiavazzi, Andrea Zanoni
https://arxiv.org/abs/2506.08921

Enabling stratified sampling in high dimensions via nonlinear dimensionality reduction
We consider the problem of propagating the uncertainty from a possibly large number of random inputs through a computationally expensive model. Stratified sampling is a well-known variance reduction strategy, but its application, thus far, has focused on models with a limited number of inputs due to the challenges of creating uniform partitions in high dimensions. To overcome these challenges, we perform stratification with respect to the uniform distribution defined over the unit interval, and…

This https://arxiv.org/abs/2408.17151 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csCR_…

Investigating Privacy Leakage in Dimensionality Reduction Methods via Reconstruction Attack
This study investigates privacy leakage in dimensionality reduction methods through a novel machine learning-based reconstruction attack. Employing an informed adversary threat model, we develop a neural network capable of reconstructing high-dimensional data from low-dimensional embeddings. We evaluate six popular dimensionality reduction techniques: principal component analysis (PCA), sparse random projection (SRP), multidimensional scaling (MDS), Isomap, $t$-distributed stochastic neighbor…

RePlaChem: A dimensionality reduction library for plasma chemical mechanisms
Z. Nikolaou, E. Morais, S. Van Rompaey, C. Anastassiou, A. Bogaerts, V. Vavourakis
https://arxiv.org/abs/2507.04401

RePlaChem: A dimensionality reduction library for plasma chemical mechanisms
In this work, we present RePlaChem, a software library for reducing detailed large-scale plasma chemical mechanisms to smaller skeletal ones. The library parses a plasma chemical mechanism in the well-established format compatible with the software ZDPlasKin, runs the reduction algorithm, and generates automatically a skeletal chemical mechanism. This feature allows the seamless implementation of the skeletal chemistry in well-known solvers including ZDPlasKin. In turn, the reduction of the che…

PromotionGo at SemEval-2025 Task 11: A Feature-Centric Framework for Cross-Lingual Multi-Emotion Detection in Short Texts
Ziyi Huang, Xia Cui
https://arxiv.org/abs/2507.08499

PromotionGo at SemEval-2025 Task 11: A Feature-Centric Framework for Cross-Lingual Multi-Emotion Detection in Short Texts
This paper presents our system for SemEval 2025 Task 11: Bridging the Gap in Text-Based Emotion Detection (Track A), which focuses on multi-label emotion detection in short texts. We propose a feature-centric framework that dynamically adapts document representations and learning algorithms to optimize language-specific performance. Our study evaluates three key components: document representation, dimensionality reduction, and model training in 28 languages, highlighting five for detailed anal…

This https://arxiv.org/abs/2505.20929 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csSI_…

Two-step dimensionality reduction of human mobility data: From potential landscapes to spatiotemporal insights
Understanding the spatiotemporal patterns of human mobility is crucial for addressing societal challenges, such as epidemic control and urban transportation optimization. Despite advancements in data collection, the complexity and scale of mobility data continue to pose significant analytical challenges. Existing methods often result in losing location-specific details and fail to fully capture the intricacies of human movement. This study proposes a two-step dimensionality reduction framework …

Hierarchical Interaction Summarization and Contrastive Prompting for Explainable Recommendations
Yibin Liu, Ang Li, Shijian Li
https://arxiv.org/abs/2507.06044

Hierarchical Interaction Summarization and Contrastive Prompting for Explainable Recommendations
Explainable recommendations, which use the information of user and item with interaction to generate a explanation for why the user would interact with the item, are crucial for improving user trust and decision transparency to the recommender system. Existing methods primarily rely on encoding features of users and items to embeddings, which often leads to information loss due to dimensionality reduction, sparse interactions, and so on. With the advancements of large language models (LLMs) in …

HCVR: A Hybrid Approach with Correlation-aware Voting Rules for Feature Selection
Nikita Bhedasgaonkar, Rushikesh K. Joshi
https://arxiv.org/abs/2507.02073

HCVR: A Hybrid Approach with Correlation-aware Voting Rules for Feature Selection
In this paper, we propose HCVR (Hybrid approach with Correlation-aware Voting Rules), a lightweight rule-based feature selection method that combines Parameter-to-Parameter (P2P) and Parameter-to-Target (P2T) correlations to eliminate redundant features and retain relevant ones. This method is a hybrid of non-iterative and iterative filtering approaches for dimensionality reduction. It is a greedy method, which works by backward elimination, eliminating possibly multiple features at every step.…

Deep Symmetric Autoencoders from the Eckart-Young-Schmidt Perspective
Simone Brivio, Nicola Rares Franco
https://arxiv.org/abs/2506.11641 https://

Deep Symmetric Autoencoders from the Eckart-Young-Schmidt Perspective
Deep autoencoders have become a fundamental tool in various machine learning applications, ranging from dimensionality reduction and reduced order modeling of partial differential equations to anomaly detection and neural machine translation. Despite their empirical success, a solid theoretical foundation for their expressiveness remains elusive, particularly when compared to classical projection-based techniques. In this work, we aim to take a step forward in this direction by presenting a com…

Beamforming with Random Projections: Upper and Lower Bounds
Manan Mittal, Ryan M. Corey, Andrew C. Singer
https://arxiv.org/abs/2507.05662 https://

Beamforming with Random Projections: Upper and Lower Bounds
Beamformers often trade off white noise gain against the ability to suppress interferers. With distributed microphone arrays, this trade-off becomes crucial as different arrays capture vastly different magnitude and phase differences for each source. We propose the use of multiple random projections as a first-stage preprocessing scheme in a data-driven approach to dimensionality reduction and beamforming. We show that a mixture beamformer derived from the use of multiple such random projection…

Similarity Matching Networks: Hebbian Learning and Convergence Over Multiple Time Scales
Veronica Centorrino, Francesco Bullo, Giovanni Russo
https://arxiv.org/abs/2506.06134

Similarity Matching Networks: Hebbian Learning and Convergence Over Multiple Time Scales
A recent breakthrough in biologically-plausible normative frameworks for dimensionality reduction is based upon the similarity matching cost function and the low-rank matrix approximation problem. Despite clear biological interpretation, successful application in several domains, and experimental validation, a formal complete convergence analysis remains elusive. Building on this framework, we consider and analyze a continuous-time neural network, the \emph{similarity matching network}, for pri…

This https://arxiv.org/abs/2502.00168 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_sta…

Supervised Quadratic Feature Analysis: Information Geometry Approach for Dimensionality Reduction
Supervised dimensionality reduction aims to map labeled data to a low-dimensional feature space while maximizing class discriminability. Directly computing discriminability is often impractical, so an alternative approach is to learn features that maximize a distance or dissimilarity measure between classes. The Fisher-Rao distance is an important information geometry distance in statistical manifolds. It is induced by the Fisher information metric, a tool widely used for understanding neural r…

Over the Stability Space of a Multivariate Time Series
Roberto V\'asquez-Mart\'inez, Graciela Gonz\'alez-Far\'ias, Jos\'e Ulises M\'arquez Urbina, Francisco Corona
https://arxiv.org/abs/2506.22407

Over the Stability Space of a Multivariate Time Series
This paper jointly addresses the challenges of non-stationarity and high dimensionality in analysing multivariate time series. Building on the classical concept of cointegration, we introduce a more flexible notion, called stability space, aimed at capturing stationary components in settings where traditional assumptions may not hold. We examine the parametric Johansen procedure alongside two non-parametric alternatives based on dimensionality reduction techniques: Partial Least Squares and Pri…

Robust Semi-Supervised CT Radiomics for Lung Cancer Prognosis: Cost-Effective Learning with Limited Labels and SHAP Interpretation
Mohammad R. Salmanpour, Amir Hossein Pouria, Sonia Falahati, Shahram Taeb, Somayeh Sadat Mehrnia, Ali Fathi Jouzdani, Mehrdad Oveisi, Ilker Hacihaliloglu, Arman Rahmim
https://arxiv.org/abs/2507.0818…

Robust Semi-Supervised CT Radiomics for Lung Cancer Prognosis: Cost-Effective Learning with Limited Labels and SHAP Interpretation
Background: CT imaging is vital for lung cancer management, offering detailed visualization for AI-based prognosis. However, supervised learning SL models require large labeled datasets, limiting their real-world application in settings with scarce annotations. Methods: We analyzed CT scans from 977 patients across 12 datasets extracting 1218 radiomics features using Laplacian of Gaussian and wavelet filters via PyRadiomics Dimensionality reduction was applied with 56 feature selection and ex…

This https://arxiv.org/abs/2503.04672 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_…

Finding White Dwarfs' Hidden Companions using an Unsupervised Machine Learning Technique
White dwarfs (WD) with main-sequence (MS) companions are crucial probes of stellar evolution. However, due to the significant difference in their luminosities, the WD is often outshined by the MS star. The aim of this work is to find hidden companions in Gaia's sample of WD candidates. Our methodology involves applying an unsupervised machine learning algorithm for dimensionality reduction and clustering, known as Self-Organizing Map (SOM), to Gaia BP/RP (XP) spectra. This strategy allows us to…

This https://arxiv.org/abs/2505.20929 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csSI_…

Two-step dimensionality reduction of human mobility data: From potential landscapes to spatiotemporal insights
Understanding the spatiotemporal patterns of human mobility is crucial for addressing societal challenges, such as epidemic control and urban transportation optimization. Despite advancements in data collection, the complexity and scale of mobility data continue to pose significant analytical challenges. Existing methods often result in losing location-specific details and fail to fully capture the intricacies of human movement. This study proposes a two-step dimensionality reduction framework …

Assessing parameter identifiability of a hemodynamics PDE model using spectral surrogates and dimension reduction
Mitchel J. Colebank
https://arxiv.org/abs/2506.04538

Assessing parameter identifiability of a hemodynamics PDE model using spectral surrogates and dimension reduction
Computational inverse problems for biomedical simulators suffer from limited data and relatively high parameter dimensionality. This often requires sensitivity analysis, where parameters of the model are ranked based on their influence on the specific quantities of interest. This is especially important for simulators used to build medical digital twins, as the amount of data is typically limited. For expensive models, such as blood flow models, emulation is employed to expedite the simulation …

Macroscopic dynamics of oscillator ensembles with communities, higher-order interactions, and phase lags
Sabina Adhikari, Juan G. Restrepo, Per Sebastian Skardal
https://arxiv.org/abs/2507.07956

Macroscopic dynamics of oscillator ensembles with communities, higher-order interactions, and phase lags
We study the effects of phase-frustrated, higher-order interactions in a system of coupled phase oscillators with two communities. We use dimensionality reduction techniques to derive a low-dimensional system of ODEs to describe the macroscopic behavior of the system. By analyzing this system we show that, in addition to the fixed point solutions present in a system of oscillators with higher order interactions and community structure only, the system also exhibits oscillatory or chaotic synchr…

Model order reduction techniques for the stochastic finite volume method
Ray Qu, Jesse Chan, Svetlana Tokareva
https://arxiv.org/abs/2507.05091 https://

Model order reduction techniques for the stochastic finite volume method
The stochastic finite volume method (SFV method) is a high-order accurate method for uncertainty quantification (UQ) in hyperbolic conservation laws. However, the computational cost of SFV method increases for high-dimensional stochastic parameter spaces due to the curse of dimensionality. To address this challenge, we incorporate interpolation-based reduced order modeling (ROM) techniques that reduce the cost of computing stochastic integrals in SFV method. Further efficiency gains are achieve…

A Fast Iterative Robust Principal Component Analysis Method
Timbwaoga Aime Judicael Ouermi, Jixian Li, Chris R. Johnson
https://arxiv.org/abs/2506.16013 ht…

A Fast Iterative Robust Principal Component Analysis Method
Principal Component Analysis (PCA) is widely used for dimensionality reduction and data analysis. However, PCA results are adversely affected by outliers often observed in real-world data. Existing robust PCA methods are often computationally expensive or exhibit limited robustness. In this work, we introduce a Fast Iterative Robust (FIR) PCA method by efficiently estimating the inliers center location and covariance. Our approach leverages Incremental PCA (IPCA) to iteratively construct a subs…

Machine Learning Based Stress Testing Framework for Indian Financial Market Portfolios
Vidya Sagar G, Shifat Ali, Siddhartha P. Chakrabarty
https://arxiv.org/abs/2507.02011

Machine Learning Based Stress Testing Framework for Indian Financial Market Portfolios
This paper presents a machine learning driven framework for sectoral stress testing in the Indian financial market, focusing on financial services, information technology, energy, consumer goods, and pharmaceuticals. Initially, we address the limitations observed in conventional stress testing through dimensionality reduction and latent factor modeling via Principal Component Analysis and Autoencoders. Building on this, we extend the methodology using Variational Autoencoders, which introduces …

SecureFed: A Two-Phase Framework for Detecting Malicious Clients in Federated Learning
Likhitha Annapurna Kavuri, Akshay Mhatre, Akarsh K Nair, Deepti Gupta
https://arxiv.org/abs/2506.16458

SecureFed: A Two-Phase Framework for Detecting Malicious Clients in Federated Learning
Federated Learning (FL) protects data privacy while providing a decentralized method for training models. However, because of the distributed schema, it is susceptible to adversarial clients that could alter results or sabotage model performance. This study presents SecureFed, a two-phase FL framework for identifying and reducing the impact of such attackers. Phase 1 involves collecting model updates from participating clients and applying a dimensionality reduction approach to identify outlier…

Enhancing Interpretability in Generative Modeling: Statistically Disentangled Latent Spaces Guided by Generative Factors in Scientific Datasets
Arkaprabha Ganguli, Nesar Ramachandra, Julie Bessac, Emil Constantinescu
https://arxiv.org/abs/2507.00298

Enhancing Interpretability in Generative Modeling: Statistically Disentangled Latent Spaces Guided by Generative Factors in Scientific Datasets
This study addresses the challenge of statistically extracting generative factors from complex, high-dimensional datasets in unsupervised or semi-supervised settings. We investigate encoder-decoder-based generative models for nonlinear dimensionality reduction, focusing on disentangling low-dimensional latent variables corresponding to independent physical factors. Introducing Aux-VAE, a novel architecture within the classical Variational Autoencoder framework, we achieve disentanglement with m…

Active Learning for Manifold Gaussian Process Regression
Yuanxing Cheng, Lulu Kang, Yiwei Wang, Chun Liu
https://arxiv.org/abs/2506.20928 https://

Active Learning for Manifold Gaussian Process Regression
This paper introduces an active learning framework for manifold Gaussian Process (GP) regression, combining manifold learning with strategic data selection to improve accuracy in high-dimensional spaces. Our method jointly optimizes a neural network for dimensionality reduction and a Gaussian process regressor in the latent space, supervised by an active learning criterion that minimizes global prediction error. Experiments on synthetic data demonstrate superior performance over randomly sequen…

Covariance Supervised Principal Component Analysis
Theodosios Papazoglou, Guosheng Yin
https://arxiv.org/abs/2506.19247 https://arxiv…

Covariance Supervised Principal Component Analysis
Principal component analysis (PCA) is a widely used unsupervised dimensionality reduction technique in machine learning, applied across various fields such as bioinformatics, computer vision and finance. However, when the response variables are available, PCA does not guarantee that the derived principal components are informative to the response variables. Supervised PCA (SPCA) methods address this limitation by incorporating response variables into the learning process, typically through an o…

Bayesian integrative factor analysis methods, with application in nutrition and genomics data
Mavis Liang, Blake Hansen, Alejandra Avalos-Pacheco, Roberta De Vito
https://arxiv.org/abs/2506.18479

Bayesian integrative factor analysis methods, with application in nutrition and genomics data
High-dimensional data are crucial in biomedical research. Integrating such data from multiple studies is a critical process that relies on the choice of advanced statistical models, enhancing statistical power, reproducibility, and scientific insight compared to analyzing each study separately. Factor analysis (FA) is a core dimensionality reduction technique that models observed data through a small set of latent factors. Bayesian extensions of FA have recently emerged as powerful tools for mu…