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. In this paper, we propose a feature selection method exploiting the convergence properties of power series of matrices, and introducing the concept of infinite feature selection (Inf-FS). Considering a selection of features as a path among feature distributions and letting these paths tend to an infinite number permits the investigation of the importance (relevance and redundancy) of a feature when injected into an arbitrary set of cues. Ranking the importance individuates candidate features, which turn out to be effective from a classification point of view, as proved by a thoroughly experimental section. The Inf-FS has been tested on thirteen diverse benchmarks, comparing against filters, embedded methods, and wrappers; in all the cases we achieve top performances, notably on the classification tasks of PASCAL VOC 2007-2012.
Code (1)
Tasks
Classificationfeature selectionGeneral ClassificationObject RecognitionSimilar Papers 제목 키워드 기반
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 re…
feature selectionSupervised Infinite Feature Selection
In this paper, we present a new feature selection method that is suitable for both unsupervised and supervised problems. We build upon the recently proposed Infinite Feature Selection (IFS) method where feature subsets o…
feature selectionGeneral Classificationimage-classificationImage ClassificationThe Replicator Dynamics for Multilevel Selection in Evolutionary Games
We consider a stochastic model for evolution of group-structured populations in which interactions between group members correspond to the Prisoner's Dilemma or the Hawk-Dove game. Selection operates at two organization …
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. A feature selection mechanism is embedded in the Adaptive colour Names (CN) trackin…
feature selectionObjectObject TrackingPosition+1Bayesian feature selection with strongly-regularizing priors maps to the Ising Model
Identifying small subsets of features that are relevant for prediction and/or classification tasks is a central problem in machine learning and statistics. The feature selection task is especially important, and computat…
Bayesian Inferencefeature selectionregression