Papers Supervised dimensionality reduction
“Supervised dimensionality reduction” 태그가 달린 논문 57편 · 필터 해제
CASE -- Condition-Aware Sentence Embeddings for Conditional Semantic Textual Similarity Measurement
The meaning conveyed by a sentence often depends on the context in which it appears. Despite the progress of sentence embedding methods, it remains unclear how to best modify a sentence embedding conditioned on its conte…
Dimensionality ReductionLanguage ModelingLanguage ModellingLarge Language Model+7A Convex formulation for linear discriminant analysis
We present a supervised dimensionality reduction technique called Convex Linear Discriminant Analysis (ConvexLDA). The proposed model optimizes a multi-objective cost function by balancing two complementary terms. The fi…
Dimensionality ReductionSupervised dimensionality reductionSupervised 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 approac…
Dimensionality ReductionMetric LearningSupervised dimensionality reductionOn the Inherent Robustness of One-Stage Object Detection against Out-of-Distribution Data
Robustness is a fundamental aspect for developing safe and trustworthy models, particularly when they are deployed in the open world. In this work we analyze the inherent capability of one-stage object detectors to robus…
Dimensionality ReductionObjectobject-detectionObject Detection+1An Embedding is Worth a Thousand Noisy Labels
The performance of deep neural networks scales with dataset size and label quality, rendering the efficient mitigation of low-quality data annotations crucial for building robust and cost-effective systems. Existing stra…
Dimensionality ReductionSupervised dimensionality reductionEnhancing Supervised Visualization through Autoencoder and Random Forest Proximities for Out-of-Sample Extension
The value of supervised dimensionality reduction lies in its ability to uncover meaningful connections between data features and labels. Common dimensionality reduction methods embed a set of fixed, latent points, but ar…
Dimensionality ReductionSupervised dimensionality reductionGradient Boosting Mapping for Dimensionality Reduction and Feature Extraction
A fundamental problem in supervised learning is to find a good set of features or distance measures. If the new set of features is of lower dimensionality and can be obtained by a simple transformation of the original da…
Dimensionality ReductionregressionSupervised dimensionality reductionCurvature Augmented Manifold Embedding and Learning
A new dimensional reduction (DR) and data visualization method, Curvature-Augmented Manifold Embedding and Learning (CAMEL), is proposed. The key novel contribution is to formulate the DR problem as a mechanistic/physics…
Data VisualizationDimensionality ReductionMetric LearningSupervised dimensionality reductionLearning Active Subspaces and Discovering Important Features with Gaussian Radial Basis Functions Neural Networks
Providing a model that achieves a strong predictive performance and is simultaneously interpretable by humans is one of the most difficult challenges in machine learning research due to the conflicting nature of these tw…
Decision MakingDimensionality Reductionfeature selectionSupervised dimensionality reductionSupervised Manifold Learning via Random Forest Geometry-Preserving Proximities
Manifold learning approaches seek the intrinsic, low-dimensional data structure within a high-dimensional space. Mainstream manifold learning algorithms, such as Isomap, UMAP, $t$-SNE, Diffusion Map, and Laplacian Eigenm…
Dimensionality ReductionSupervised dimensionality reductionYet Another Algorithm for Supervised Principal Component Analysis: Supervised Linear Centroid-Encoder
We propose a new supervised dimensionality reduction technique called Supervised Linear Centroid-Encoder (SLCE), a linear counterpart of the nonlinear Centroid-Encoder (CE) \citep{ghosh2022supervised}. SLCE works by mapp…
Dimensionality ReductionSupervised dimensionality reductionK-SpecPart: Supervised embedding algorithms and cut overlay for improved hypergraph partitioning
State-of-the-art hypergraph partitioners follow the multilevel paradigm that constructs multiple levels of progressively coarser hypergraphs that are used to drive cut refinement on each level of the hierarchy. Multileve…
Dimensionality Reductionhypergraph partitioningSupervised dimensionality reductionNon-intrusive surrogate modelling using sparse random features with applications in crashworthiness analysis
Efficient surrogate modelling is a key requirement for uncertainty quantification in data-driven scenarios. In this work, a novel approach of using Sparse Random Features for surrogate modelling in combination with self-…
Dimensionality ReductionSupervised dimensionality reductionUncertainty QuantificationGravitational Dimensionality Reduction Using Newtonian Gravity and Einstein's General Relativity
Due to the effectiveness of using machine learning in physics, it has been widely received increased attention in the literature. However, the notion of applying physics in machine learning has not been given much awaren…
Dimensionality ReductionMetric LearningSupervised dimensionality reductionAffective Manifolds: Modeling Machine's Mind to Like, Dislike, Enjoy, Suffer, Worry, Fear, and Feel Like A Human
After the development of different machine learning and manifold learning algorithms, it may be a good time to put them together to make a powerful mind for machine. In this work, we propose affective manifolds as compon…
Dimensionality ReductionMetric LearningSupervised dimensionality reductionSupervised Dimensionality Reduction and Image Classification Utilizing Convolutional Autoencoders
The joint optimization of the reconstruction and classification error is a hard non convex problem, especially when a non linear mapping is utilized. In order to overcome this obstacle, a novel optimization strategy is p…
Deep LearningDimensionality ReductionGeneral Classificationimage-classification+2Taking a Step Back with KCal: Multi-Class Kernel-Based Calibration for Deep Neural Networks
Deep neural network (DNN) classifiers are often overconfident, producing miscalibrated class probabilities. In high-risk applications like healthcare, practitioners require $\textit{fully calibrated}$ probability predict…
Decision MakingDensity EstimationDimensionality ReductionNetwork Embedding+1SLISEMAP: Supervised dimensionality reduction through local explanations
Existing methods for explaining black box learning models often focus on building local explanations of model behaviour for a particular data item. It is possible to create global explanations for all data items, but the…
ClassificationDimensionality ReductionExplainable ModelsGPU+1Discriminant Analysis in Contrasting Dimensions for Polycystic Ovary Syndrome Prognostication
A lot of prognostication methodologies have been formulated for early detection of Polycystic Ovary Syndrome also known as PCOS using Machine Learning. PCOS is a binary classification problem. Dimensionality Reduction me…
BIG-bench Machine LearningBinary ClassificationClassificationDimensionality Reduction+1Scalable semi-supervised dimensionality reduction with GPU-accelerated EmbedSOM
Dimensionality reduction methods have found vast application as visualization tools in diverse areas of science. Although many different methods exist, their performance is often insufficient for providing quick insight …
Data VisualizationDimensionality ReductionGPUSupervised dimensionality reduction