Rank correlation under categorical confounding

Rank correlation under categorical confounding

Correspondence: [email protected] Department of Decision Sciences, HEC Montréal, 3000 chemin de la Côte-Sainte-Catherine, H3T 2A7 Montréal, Canada Abstract Rank correlation is invariant to bijective marginal transformations, but it is not immune to confounding. Assuming a categorical confounding variable is observed, the author proposes weighted coefficients of correlation for continuous variable...

Color Texture Classification under Different Illuminations Using Rank Correlation Matrices

Color has been shown to be useful in the context of texture classification. However, since under different illuminations color is not stable, color invariant descriptors should be defined when the illumination of the query is unknown. In this paper, we propose to characterize color textures by analyzing the rank correlation of color planes between pixels locally close to each other. Thus, consi...

Similarity Rank Correlation for Face Recognition Under Unenrolled Pose

Face recognition systems have to deal with the problem that not all variations of all persons can be enrolled. Rather, the variations of most persons must be modeled. Explicit modeling of different poses is awkward and time consuming. Here, we present a subsystem that builds a model of pose variation by keeping a model database of persons in both poses, additionally to the gallery of clients kn...

Spearman Rank Correlation Coefficient

Generalized Adjustment Under Confounding and Selection Biases

Selection and confounding biases are the two most common impediments to the applicability of causal inference methods in large-scale settings. We generalize the notion of backdoor adjustment to account for both biases and leverage external data that may be available without selection bias (e.g., data from census). We introduce the notion of adjustment pair and present complete graphical conditi...