Papers Dimensionality Reduction
“Dimensionality Reduction” 태그가 달린 논문 3,716편 · 필터 해제
Reduced-Space Multi-Fidelity Bayesian Optimization of Process Simulation Models
Optimizing industrial process flowsheets is often computationally prohibitive due to the high cost of rigorous simulations and the curse of dimensionality inherent in complex design spaces. To address these challenges, w…
Dimensionality ReductionMultimodal Cultural Heritage Architectural Style Classification for Residential Buildings in the UAE Based on CLIP Embeddings and SVM
The analysis and classification of cultural heritage architectural styles remain challenging due to the complexity of visual images of buildings, which are highly relied on in traditional CNN-based classification approac…
Dimensionality ReductionDimensionality Reduction for Hyperspectral Image Classification
This paper addresses the issue of supervised classification in the context of hyperspectral satellite images. It deals with two fundamental aspects: dimensionality reduction of data and the selection of appropriate super…
Hyperspectral Image ClassificationDimensionality ReductionHybrid Quantum-Classical NLP Classification with Compact Semantic Representations: An Experimental Analysis of Representation Compression
Large language and sentence-embedding models provide rich semantic representations, but their high dimensionality poses a challenge for near-term quantum machine learning (QML), where quantum circuits can process only a …
Quantum Machine LearningDimensionality ReductionEfficient Estimation of High Information Projections using Nearest Neighbours
An intuitive method for dimensionality reduction is proposed, which is highly effective for finding interesting projections of multivariate data. Following similar intuitive motivation to a number of existing techniques,…
Dimensionality ReductionOutlier DetectionAristotelian Manifolds: Leveraging Platonic Perceptual Features for Backpropagation Free Rapid Concept Learning
This paper formalizes and systematically characterizes Aristotelian Manifolds, a generalized structural framework built upon the Platonic Representation Hypothesis. We position high-capacity foundation models as universa…
Dimensionality ReductionHAF: Adapting Generalist VLAs to Humanoid Whole-Body Loco-manipulation via Hierarchical Action Flow and Spectral Latent RL
Humanoid robots hold great promise as general-purpose agents in human-centered environments, yet generalist vision-language-action (VLA) foundation models are not readily applicable to humanoid whole-body loco-manipulati…
Dimensionality ReductionReinforcement LearningA Multispectral Framework for the Detection of Calcium Carbide-Induced Ripening and Shelf-Life Estimation in Climacteric Fruits
Significant health risks are associated with the illegal, yet commonly practiced use of industrial-grade Calcium Carbide (CaC2) for ripening climacteric fruits like mango and banana, which leaves behind trace residues of…
Dimensionality ReductionFeature EngineeringConvergent Evolution in Neural Representation Space: Emergent Order in Deep Belief Networks
Deep Belief Networks (DBNs) learn hierarchical generative models without class supervision. Here, we ask whether this purely unsupervised process nevertheless organizes internal representations according to the unknown d…
Dimensionality ReductionGROM: Gradient-Free Rapid One-Shot Machine Unlearning
Machine unlearning has become a critical capability for safely removing specific, sensitive knowledge from large language models (LLMs). Current state-of-the-art approaches primarily rely on iterative, training-time unle…
Dimensionality ReductionFrom field-scale to large-scale spectral libraries: Tabular foundation models in soil spectroscopy
Visible and near-infrared (vis-NIR) and mid-infrared (MIR) spectroscopy enable rapid, cost-effective prediction of soil properties. Yet, translating high-dimensional, highly collinear spectra into accurate soil property …
Dimensionality ReductionMeasuring Distortion in the Empty Regions of Dimensionality Reduction Scatterplots with the Gap Index
Quality metrics play a crucial role in the proper use of dimensionality reduction projections for visual analysis of high-dimensional data. They quantify the degree of distortion of a projection compared to the high-dime…
Dimensionality ReductionNon--negative matrix factorization using the \textit{R} package \textsf{nnmf}
Non--negative matrix factorization (NMF) has become an established dimensionality reduction technique for extracting latent structures from non--negative data and has found widespread applications in fields such as bioin…
Computational EfficiencyDimensionality ReductionStructured Fluctuations and the Information Dynamics of Self-Maintenance in Growing Neural Cellular Automata
Growing Neural Cellular Automata (GNCA) are capable of robust self-maintenance and self-repair, yet the internal dynamical mechanisms that support these capabilities remain poorly understood. Here, we investigate the rol…
Dimensionality ReductionEmergent Generalization by Representation Learning in Artificial Neural Networks
Dimensionality reduction has proven powerful for identifying neural manifolds, which are low-dimensional structures underlying high-dimensional neural activity. These low-dimensional representations have improved the int…
Dimensionality ReductionRepresentation LearningQuantum-Enhanced Synthetic Data Generation Using Quantum Circuit Born Machines for Imbalanced Tabular Learning
Data scarcity and class imbalance are persistent challenges in machine learning that degrade model generalization and introduce predictive bias. We present a hybrid quantum-classical framework for synthetic data generati…
Synthetic Data GenerationDimensionality ReductionData AugmentationDimensionality Reduction Meets Network Science: Sensemaking on UMAP's kNN Graph
While UMAP is widely used for exploring high-dimensional data, typical workflows focus on its lower-dimensional embedding, largely overlooking the rich k-nearest-neighbor (kNN) graph that UMAP constructs internally. This…
Dimensionality ReductionGroup Invariant Spectral Embedding
Spectral embedding methods are widely used for dimensionality reduction and clustering of high-dimensional datasets with intrinsic low-dimensional structures. Although many datasets of practical interest exhibit invarian…
Dimensionality ReductionNeural Operator-enabled Topology-informed Evolutionary Strategy for PDE-Constrained Optimization
The inverse design of physical systems governed by partial differential equations is computationally demanding due to the high dimensionality and non-convexity of design spaces. Generative models for inverse design often…
Dimensionality ReductionRepresentation LearningAn Hybrid Quantum-Classical Diffusion Model for Image Generation
Quantum diffusion models provide a physics-consistent route to generative learning by formulating noising and denoising directly on quantum states. However, applying such models to classical high-dimensional data is cons…
Dimensionality ReductionImage Generation