Supervised dimensionality reduction

Least squares regression principal component analysis

This article proposes a supervised dimensionality reduction method, “least squares regression principal component analysis” (LSR‐PCA), applicable to both ...

Supervised Dimensionality Reduction of Proportional Data Using ...

Most well-known supervised dimensionality reduction algorithms suffer from the curse of dimensionality while handling high-dimensional sparse data due to ...

A supervised take on dimensionality reduction via hybrid subset ...

present HSS-LDA, a supervised dimensionality reduction approach for single-cell data that outperforms existing unsupervised techniques. They ...

Supervised dimensionality reduction technique accounting for soft ...

Supervised dimensionality reduction technique accounting for soft classes. Sorina Mustatea1,2, Michaël Aupetit3, Jaakko Peltonen4,. Sylvain ...

Dimensionality reduction - Wikipedia

Dimensionality reduction, or dimension reduction, is the transformation of data from a high-dimensional space into a low-dimensional space so that the ...

6 Dimensionality Reduction Algorithms With Python

Dimensionality reduction refers to techniques for reducing the number of input variables in training data. When dealing with high dimensional ...

Supervised (a.k.a. Task-specific) dimensionality reduction - Medium

Supervised (a.k.a. Task-specific) dimensionality reduction ... We often encounter data with more features/dimensions than we know what to do with.

Supervised Nonlinear Dimensionality Reduction for Visualization ...

Such a kind of procedure is called supervised nonlinear dimensionality reduction. In S-Isomap, the neighborhood graph of the input data is constructed according ...

A Perception-Driven Approach to Supervised Dimensionality ...

Abstract—Dimensionality reduction (DR) is a common strategy for visual analysis of labeled high-dimensional data. Low-dimensional.

Supervised dimensionality reduction using mixture models

Supervised dimensionality reduction using mixture models ... Given a classification problem, our goal is to find a low-dimensional linear transformation of the ...

Supervised Dimensionality Reduction on Streaming Data

Streaming data are time variant and can be in high dimensions. When a sliding window is moving along data stream, the data that have passed out of the window ...

Review of Dimension Reduction Methods

A semi supervised variant of LDA was proposed by [151] with its main objective of combining both labeled and unlabeled data for training LDA and to allow for ...

Fast supervised dimensionality reduction algorithm with applications ...

Fast supervised dimensionality reduction algorithm with applications to document categorization & retrieval

Top 12 Dimensionality Reduction Techniques - Analytics Vidhya

3. Common Dimensionality Reduction Techniques · 3.1 Missing Value Ratio · 3.2 Low Variance Filter · 3.3 High Correlation filter · 3.4 Random Forest.

Least squares regression principal component analysis: A ...

Dimensionality reduction is an important technique in surrogate modeling and machine learning. In this article, we propose a supervised ...

SLDR: supervised linear dimensionality reduction toolbox - MathWorks

A MATLAB toolbox for supervised linear dimension reduction (SLDR) including LDA, HLDA, PLSDA, MMDA, HMMDA and SDA

Dimensionality Reduction with Neighborhood Components Analysis

In particular, LDA, in contrast to PCA, is a supervised method, using known class labels. Neighborhood Components Analysis (NCA) tries to find a feature space ...

A combination of supervised dimensionality reduction and learning ...

García-Cuesta, E., Aler, R., Pózo-Vázquez, D.d. et al. A combination of supervised dimensionality reduction and learning methods to forecast solar radiation.

Supervised dimensionality reduction technique accounting for soft ...

Exploratory visual analysis of multidimensional labeled data is challenging. Multidimensional Projections for labeled data attempt to separate classes while ...

On Sequence Clustering and Supervised Dimensionality Reduction

For dimensionality reduction, the focus is on classification problem where label information in the training data can be leveraged for improved learning ...