When: Nov. 28 2019 (Thurs) 16:00--17:15
Where: Bldg. 110 Rm. N103

Speaker: Hosoo Lee (Jeju National University)

Title: Rimannian Dictionary Learning

Abstract: Data encoded as symmetric positive definite matrices frequently arise in many areas of computer vision and machine learning. While these matrices form an open subset of the Euclidean space of symmetric matrices, viewing them through the lens of non-Euclidean Riemannian geometry often turns out to be better suited in capturing several desirable data properties. However, formulating classical machine learning algorithms within such a geometry is often non-trivial and computationally expensive. Inspired by the great success of dictionary learning and sparse coding for vector-valued data, our goal in this talk is to represent data in the form of SPD matrices as sparse conic combinations of SPD atoms from a learned dictionary via a Riemannian geometric approach.