A proposal of l1 regularized distance metric learning for high dimensional sparse vector space.

Kenta Mikawa Manabu Kobayashi Masayuki Goto Shigeichi Hirasawa

Published in: SMC (2014)

Keyphrases

vector space
high dimensional
distance metric learning
low dimensional
nonlinear dimensionality reduction
similarity search
metric learning
distance metric
manifold learning

dimensionality reduction
high dimensional data
distance function
euclidean space
data points
metric space
image classification
feature space
gender classification

distance measure
dimension reduction
locally linear embedding
semidefinite programming
semi supervised
nearest neighbor
input space
least squares
covariance matrices

objective function
riemannian manifolds
parameter space
multi task
kernel learning
feature vectors
kernel function
multi class
sparse representation