- A graph theoretic approach for unsupervised feature selection🔍
- A graph theoretic approach for unsupervised feature selection ...🔍
- Unsupervised Non|redundant Feature Selection🔍
- A graph theoretic approach for unsupervised feature selection 🔍
- Unsupervised Feature Selection Using Graph Theoretic Approach ...🔍
- Unsupervised feature selection using graph theoretic approach🔍
- An information|theoretic graph|based approach for feature selection🔍
- Graph|Based Unsupervised Feature Selection for Interval|Valued ...🔍
A graph theoretic approach for unsupervised feature selection.
A graph theoretic approach for unsupervised feature selection
Highlights. •. A novel graph-theoretic approach for unsupervised feature selection is proposed. •. Our method integrates graph clustering with a novel iterative ...
A graph theoretic approach for unsupervised feature selection
A novel graph-theoretic approach for unsupervised feature selection is proposed. •. Our method integrates graph clustering with a novel iterative search ...
A graph theoretic approach for unsupervised feature selection
A graph theoretic approach for unsupervised feature selection · P. Moradi, M. Rostami · Published in Engineering applications of… 1 September 2015 · Computer ...
A graph theoretic approach for unsupervised feature selection ...
Highlights•A novel graph-theoretic approach for unsupervised feature selection is proposed.•Our method integrates graph clustering with a novel iterative ...
Unsupervised Non-redundant Feature Selection: A Graph-Theoretic ...
In this article a graph-theoretic approach for non-redundant unsupervised feature selection has been presented. The input data matrix is first converted ...
A graph theoretic approach for unsupervised feature selection (2015 ...
TL;DR: The results show that the proposed graph-theoretic approach for unsupervised feature selection has produced consistently better classification ...
Unsupervised Feature Selection Using Graph Theoretic Approach ...
A method called graph-theoretic approach for unsupervised feature selection has been proposed to solve these issues. The proposed method works in three steps.
A graph theoretic approach for unsupervised feature selection - OUCI
Mandal Monalisa, Mukhopadhyay, A., 2013. Unsupervised non-redundant feature selection: a graph-theoretic approach. In: Proceedings of the International ...
Unsupervised feature selection using graph theoretic approach
Unsupervised Feature Selection Using Graph Theoretic Approach. Discovery,. 2016, 52(241), 48-54. Publication License. © The Author(s) 2016. Open Access. This ...
A graph theoretic approach for unsupervised feature selection | CoLab
Feature subset selection is a major problem in data mining which can help to reduce computation time, improve prediction performance, ...
An information-theoretic graph-based approach for feature selection
available or not, feature selection may be supervised or unsupervised. For supervised feature selection, relative importance of the features can be ...
Graph-Based Unsupervised Feature Selection for Interval-Valued ...
For these reasons, we propose a feature selection method for IVISs based on graph theory. Then, the model complexity could be greatly ...
A Graph-Based Approach to Feature Selection - SpringerLink
In many data analysis tasks, one is often confronted with very high dimensional data. The feature selection problem is essentially a combinatorial ...
Unsupervised Feature Selection Using Information-Theoretic Graph ...
The proposed algorithm—Graph-based Information-Theoretic Approach for Unsupervised Feature Selection (GITAUFS) generates multiple minimal vertex covers (MVC) of ...
A Graph-Based Approach to Feature Selection - Semantic Scholar
An algorithm consisting of three phases that separates features into clusters in advance, which allows us to limit the search space for higher order ...
A graph theoretic approach for unsupervised feature selection. - dblp
Parham Moradi , Mehrdad Rostami : A graph theoretic approach for unsupervised feature selection. Eng. Appl. Artif. Intell. 44: 33-45 (2015).
An Unsupervised Approach to Feature Selection - ACM Digital Library
We propose an unsupervised, model-agnostic, wrapper method for feature selection. We assume that if a feature can be predicted using the others, ...
We propose an unsuper- vised feature selection approach which performs feature se- lection and local structure learning simultaneously, the sim- ilarity matrix ...
An Information-theoretic Approach to Unsupervised Feature ...
Moreover, we demonstrate that these functional represen- tations can be directly computed from the observed data vectors by a multivariate alternating ...
Unsupervised feature selection with adaptive multiple graph learning
In our method, we integrate the multiple graph learning and the feature selection into a unified framework, which can jointly characterize the structure of the ...