54 citations · 162 across the 19 of their papers we have counts for
11 papers · 1 filter
Matrix Approximation under Local Low-Rank Assumption
Joonseok Lee, Seungyeon Kim, Guy Lebanon +1
Matrix approximation is a common tool in machine learning for building accurate prediction models for recommendation systems, text mining, and computer vision. A prevalent assumpti…
Learning Riemannian Metrics
Guy Lebanon
We propose a solution to the problem of estimating a Riemannian metric associated with a given differentiable manifold. The metric learning problem is based on minimizing the relat…
An Extended Cencov-Campbell Characterization of Conditional Information Geometry
Guy Lebanon
We formulate and prove an axiomatic characterization of conditional information geometry, for both the normalized and the nonnormalized cases. This characterization extends the axi…
The Landmark Selection Method for Multiple Output Prediction
Krishnakumar Balasubramanian, Guy Lebanon
Conditional modeling x \to y is a central problem in machine learning. A substantial research effort is devoted to such modeling when x is high dimensional. We consider, instead, t…
Statistical Translation, Heat Kernels and Expected Distances
Joshua Dillon, Yi Mao, Guy Lebanon +1
High dimensional structured data such as text and images is often poorly understood and misrepresented in statistical modeling. The standard histogram representation suffers from h…
A Linear Approximation to the chi^2 Kernel with Geometric Convergence
Fuxin Li, Guy Lebanon, Cristian Sminchisescu
We propose a new analytical approximation to the kernel that converges geometrically. The analytical approximation is derived with elementary methods and adapts to the input…