2020-02-20 Articles
La librairie deslib
implémente plusieurs algorithme de sélection de modèles
sur des problèmes de classifications. Mais plutôt que
de sélectionner un modèle ou un ensemble de modèle
sur toutes la base, elle sélectionne les meilleurs
modèles localement. On appelle cela la
sélection dynamique de classifieurs
(voir Sélection dynamique de modèles ).
Quelques lectures plus loin
(Generalized probabilistic principal component analysis of correlated data ), j’ai découvert
PPCA = Probabilistic PCA
qui produit des résultats équivalents mais formule
le problème d’une façon plus interprétable
(voir aussi edwards - probabilistic pca .
Je suis tombé aussi sur cet article
A Unified Framework for Structured Graph Learning via Spectral Constraints .
Il m’a fait découvrir la forme des valeurs propres de la matrice
Laplacien construite à partir d’un graphe bi-partite. La première équation
m’a intrigué car je manque de culture dans ce domaine :
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où est l’ensemble des matrices
dans
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définies positives pour me demander le maximum
de et
.
L’article suivant essaye de faire du clustering
Path-Based Spectral Clustering: Guarantees, Robustness to Outliers, and Fast Algorithms en utilisant
l’arc le plus long parmi dans le chemin le plus court reliant
deux noeuds. L’algorithme résultat est apparemment capable
de séparer deux spirales enlacées.
Deux autres articles
Online Sufficient Dimension Reduction Through Sliced Inverse Regression ,
Online PCA with Optimal Regret
m’ont ramené à quelques idées développées pour
l’implémentation d’une régression linéaire par morceaux .
Le dernier article apprend lui-aussi à réduire les dimensions
Perturbation Bounds for Procrustes, Classical Scaling, and Trilateration, with Applications to Manifold Learning . Je l’ai ouvert
car certains mots du titres me faisait penser à formules
magiques tirées de Harry Potter.