Supervised dimensionality reduction
15 papers with code • 0 benchmarks • 0 datasets
Benchmarks
These leaderboards are used to track progress in Supervised dimensionality reduction
Latest papers with no code
Supervised Manifold Learning via Random Forest Geometry-Preserving Proximities
Manifold learning approaches seek the intrinsic, low-dimensional data structure within a high-dimensional space.
Yet Another Algorithm for Supervised Principal Component Analysis: Supervised Linear Centroid-Encoder
SLCE works by mapping the samples of a class to its class centroid using a linear transformation.
K-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.
Non-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.
Taking a Step Back with KCal: Multi-Class Kernel-Based Calibration for Deep Neural Networks
Most existing calibration methods either lack theoretical guarantees for producing calibrated outputs, reduce classification accuracy in the process, or only calibrate the predicted class.
Discriminant 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.
Joint Dimensionality Reduction for Separable Embedding Estimation
In this paper, we propose a supervised dimensionality reduction method that learns linear embeddings jointly for two feature vectors representing data of different modalities or data from distinct types of entities.
Entangled Kernels -- Beyond Separability
We consider the problem of operator-valued kernel learning and investigate the possibility of going beyond the well-known separable kernels.
Steering Distortions to Preserve Classes and Neighbors in Supervised Dimensionality Reduction
Nonlinear dimensionality reduction of high-dimensional data is challenging as the low-dimensional embedding will necessarily contain distortions, and it can be hard to determine which distortions are the most important to avoid.
Manifold Partition Discriminant Analysis
We propose a novel algorithm for supervised dimensionality reduction named Manifold Partition Discriminant Analysis (MPDA).
