Dimensionality Reduction
2개 벤치마크 · 논문 3,717편 · 이 태스크의 논문 보기 →
Benchmarks
Most implemented
UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction
Adversarial Autoencoders
XGBoost: A Scalable Tree Boosting System
ECA-Net: Efficient Channel Attention for Deep Convolutional Neural Networks
Rethinking Spatial Dimensions of Vision Transformers
Towards K-means-friendly Spaces: Simultaneous Deep Learning and Clustering
Papers
Latent-Foresight: End-to-End Learning Predictable Representations for Latent World Models
Predicting the future evolution of a scene is a fundamental capability for world modeling. Recent work has shown that operating in the feature space of Vision Foundation Models (VFMs) yields semantically rich representat…
Dimensionality ReductionRepresentation LearningScene UnderstandingReduced-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 Detection