Survey on multi-output learning
The aim of multi-output learning is to simultaneously predict multiple outputs given an input.
It is an important learning problem for decision-making since making decisions in the real …
It is an important learning problem for decision-making since making decisions in the real …
Efficient kNN classification with different numbers of nearest neighbors
k nearest neighbor (kNN) method is a popular classification method in data mining and
statistics because of its simple implementation and significant classification performance …
statistics because of its simple implementation and significant classification performance …
Low-rank preserving projections
As one of the most popular dimensionality reduction techniques, locality preserving
projections (LPP) has been widely used in computer vision and pattern recognition …
projections (LPP) has been widely used in computer vision and pattern recognition …
Came: Content-and context-aware music embedding for recommendation
Traditional recommendation methods suffer from limited performance, which can be
addressed by incorporating abundant auxiliary/side information. This article focuses on a …
addressed by incorporating abundant auxiliary/side information. This article focuses on a …
Regularized label relaxation linear regression
Linear regression (LR) and some of its variants have been widely used for classification
problems. Most of these methods assume that during the learning phase, the training …
problems. Most of these methods assume that during the learning phase, the training …
Discriminative embedded clustering: A framework for grou** high-dimensional data
In many real applications of machine learning and data mining, we are often confronted with
high-dimensional data. How to cluster high-dimensional data is still a challenging problem …
high-dimensional data. How to cluster high-dimensional data is still a challenging problem …
Robust 2DPCA With Non-greedy -Norm Maximization for Image Analysis
2-D principal component analysis based on ℓ 1-norm (2DPCA-L1) is a recently developed
approach for robust dimensionality reduction and feature extraction in image domain …
approach for robust dimensionality reduction and feature extraction in image domain …
Learning to rank for blind image quality assessment
Blind image quality assessment (BIQA) aims to predict perceptual image quality scores
without access to reference images. State-of-the-art BIQA methods typically require subjects …
without access to reference images. State-of-the-art BIQA methods typically require subjects …
A generalized least-squares approach regularized with graph embedding for dimensionality reduction
In current graph embedding methods, low dimensional projections are obtained by
preserving either global geometrical structure of data or local geometrical structure of data …
preserving either global geometrical structure of data or local geometrical structure of data …
Dual-manifold regularized regression models for feature selection based on hesitant fuzzy correlation
In this paper, three novel frameworks based on the widespread regression methods Ridge,
LASSO and Elastic Net are established to perform the task of feature selection. The …
LASSO and Elastic Net are established to perform the task of feature selection. The …