1 code implementation • 15 Jun 2020 • Giorgio Roffo, Simone Melzi, Umberto Castellani, Alessandro Vinciarelli, Marco Cristani
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.
no code implementations • ICCV 2017 • Giorgio Roffo, Simone Melzi, Umberto Castellani, Alessandro Vinciarelli
An appealing characteristic of the approach is that it aims to discover an abstraction behind low-level sensory data, that is, relevancy.
no code implementations • 1 Jun 2017 • Giorgio Roffo
Ranking methods can be analyzed from two different methodological perspectives: ranking to learn and learning to rank.
no code implementations • 18 Apr 2017 • Giorgio Roffo, Simone Melzi
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.
no code implementations • 7 Sep 2016 • Giorgio Roffo, Simone Melzi
DFST proposes an optimized visual tracking algorithm based on the real-time selection of locally and temporally discriminative features.
no code implementations • 5 Jul 2016 • Giorgio Roffo
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.
1 code implementation • ICCV 2015 • Giorgio Roffo, Simone Melzi, Marco Cristani
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.