244 citations · 364 across the 5 of their papers we have counts for
7 papers
Infinite Feature Selection: A Graph-based Feature Filtering Approach
Giorgio Roffo, Simone Melzi, Umberto Castellani +2
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) r…
Infinite Latent Feature Selection: A Probabilistic Latent Graph-Based Ranking Approach
Giorgio Roffo, Simone Melzi, Umberto Castellani +1
Feature selection is playing an increasingly significant role with respect to many computer vision applications spanning from object recognition to visual object tracking. However,…
Ranking to Learn and Learning to Rank: On the Role of Ranking in Pattern Recognition Applications
Giorgio Roffo
The last decade has seen a revolution in the theory and application of machine learning and pattern recognition. Through these advancements, variable ranking has emerged as an acti…
Ranking to Learn: Feature Ranking and Selection via Eigenvector Centrality
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 otherwi…
Object Tracking via Dynamic Feature Selection Processes
Giorgio Roffo, Simone Melzi
DFST proposes an optimized visual tracking algorithm based on the real-time selection of locally and temporally discriminative features. A feature selection mechanism is embedded i…
Towards Personality-Aware Recommendation
Giorgio Roffo
In the last decade new ways of shopping online have increased the possibility of buying products and services more easily and faster than ever. In this new context, personality is…