Table A.5:

Notations: Note that throughout this paper, we refer to matrices with bold capital letters (e.g., A), vectors with small bold letters (e.g., a), and scalars or functions with all non-bold letters. $a_j^i$ is the scalar in row i and column j of A, while aⁱ the i^th row and a_j the j^th column of A.

Notation	Description
N	Number of training samples
d	Dimensionality of the feature vectors
d′	The dimensionality of the selected features set
$\mathbf{X}\in {ℝ}^{d\times N}$	Feature matrix of all samples
$\mathbf{y}\in {ℝ}^{1\times N}$	The class labels for each of the samples
${\mathbf{X}}^{\prime }\in {ℝ}^{d^{\prime }\times N}$	The new reduced feature matrix, after feature selection
k(x, xn)	Subkernel function between the two samples x and xn
α	Weights vector learned to aggregate subkernels into a kernel
k(x, xn, α)	Aggregate kernel of the two samples x and xn, using weights α
∥a∥1	The ℓ1 norm of vector a (i.e., ${\parallel \mathbf{a}\parallel }_1={\sum }_i\mid a_i\mid$)
∥a∥2	The ℓ2 norm of vector a (i.e., ${\parallel \mathbf{a}\parallel }_2={({\sum }_i{a}_i^2)}^{\frac{1}{2}}$)
∥A∥2,1	The ℓ2,1 norm of the matrix A (i.e., ${\parallel \mathbf{A}\parallel }_{2,1}≔{\sum }_j({\sum }_i{\mid a_j^i\mid }^2{)}^{\frac{1}{2}}$)
${ℝ}_{\ge 0}$	The set of non-negative real numbers