Table 2 Notations

D =  < X, y>

Labeled dataset where X ∈ R^m × n is a matrix of m instances and n features, and y ∈ {0, 1}^m is the binary class labels of the instances

x_i

i^th feature in X

g(x_i, x_j)

Function that returns the redundancy between two features x_i and x_j

f(x_i, y)

Function that returns the relevance between a feature x_i and class labels y

S

Indices of selected features

Ω

Indices of all features

Ω_S

Indices of candidate features Ω − S

k

Number of features to be selected

v

Number of views in a multi-view dataset

MVD =  < (X¹, …, X^v), y>

Labeled multi-view dataset where \( {X}^i\in {R}^{m\times {n}_i} \) is a matrix of m samples and n_i features and y ∈ {0, 1}^m is the binary class labels of the instances in all views

Dⁱ =  < Xⁱ, y>

i^th view in a multi-view dataset

\( {\mathrm{x}}_{\mathrm{j}}^{\mathrm{i}} \)

j^th feature in Xⁱ

Sⁱ

Indices of selected features from i^th view

Ωⁱ

Indices of all features in i^th view

\( {\Omega}_{S^i} \)

Indices of candidate features Ωⁱ − Sⁱ in i^th view