Estimation of Renyi information divergence via pruned minimal spanning trees
By Alfred O. Hero, O. Michel. 1999
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
- Barnab'as P'oczos
- Akshay Krishnamurthy
- Kirthevasan Kandasamy
- Jiantao Jiao
- Jeff Schneider
- Yanjun Han
- Jayadev Acharya
- Csaba Szepesvari
Citations in the corpus
- A review of feature selection methods based on mutual information (2013, methodology): “[9] were one of the first in selecting variables estimating MI in highdimensional spaces [27], which allows using set of variables instead of individual variables.”
- Minimax Estimation of Functionals of Discrete Distributions (2014, background): “For nonparametric estimation of differential entropy, the readers are referred to [80], [81], [82], [83], and [84].”
- Nonparametric Estimation of Renyi Divergence and Friends (2014, methodology): “Many of the approaches are based on nearest-neighbor graphs [5, 26, 16].”
- REGO: Rank-based Estimation of Renyi Information using Euclidean Graph Optimization (2010, background): “Hero and Michel (1999a) put a great amount of work into making their algorithm computationally efficient for a fixed k (the unmodified k -MST problem is hard), but since the algorithm needs to search for the right value of k it can be expected to be n times more expensive than a straightforward EGO…”
- Geodesic entropic graphs for dimension and entropy estimation in manifold learning (2004, background): “where its applications range from vector quantization [19,32] to pattern matching [23] and image registration [22, 31].”
- Divergence estimation of continuous distributions based on data-dependent partitions (2005, methodology): “For sources with a continuous alphabet, Hero et al. [8] provided an entropy estimation method using the minimal spanning tree which spans a set of feature vectors.”