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Found 7 papers, 2 papers with code
Infinite Feature Selection: A Graph-based Feature Filtering Approach
We propose a filtering feature selection framework that considers subsets of features as paths in a graph, where a node is a feature and an edge indicates pairwise (customizable) relations among features, dealing with relevance and redundancy principles.
Infinite Latent Feature Selection: A Probabilistic Latent Graph-Based Ranking Approach
An appealing characteristic of the approach is that it aims to discover an abstraction behind low-level sensory data, that is, relevancy.
Ranking to Learn and Learning to Rank: On the Role of Ranking in Pattern Recognition Applications
Ranking methods can be analyzed from two different methodological perspectives: ranking to learn and learning to rank.
Ranking to Learn: Feature Ranking and Selection via Eigenvector Centrality
In an era where accumulating data is easy and storing it inexpensive, feature selection plays a central role in helping to reduce the high-dimensionality of huge amounts of otherwise meaningless data.
Object Tracking via Dynamic Feature Selection Processes
DFST proposes an optimized visual tracking algorithm based on the real-time selection of locally and temporally discriminative features.
Feature Selection Library (MATLAB Toolbox)
The Feature Selection Library (FSLib) signifies a notable progression in machine learning and data mining for MATLAB users, emphasizing the critical role of Feature Selection (FS) in enhancing model efficiency and effectiveness by pinpointing essential features for specific tasks.
Infinite Feature Selection
Filter-based feature selection has become crucial in many classification settings, especially object recognition, recently faced with feature learning strategies that originate thousands of cues.
