Sep 27, 2016 · Title:Online Categorical Subspace Learning for Sketching Big Data with Misses. Authors:Yanning Shen, Morteza Mardani, Georgios B. Giannakis.
A novel categorical subspace learning approach to unravel the latent structure for three prominent categorical (bilinear) models, namely, Probit, Tobit, ...
Jun 5, 2017 · Abstract—With the scale of data growing every day, reducing the dimensionality (a.k.a. sketching) of high-dimensional data has.
Abstract—With the scale of data growing every day, reducing the dimensionality (a.k.a. sketching) of high-dimensional data has.
To cope with these challenges, the present paper develops a novel categorical subspace learning approach to unravel the latent structure for three prominent ...
To cope with these challenges, this paper develops a novel categorical subspace learning approach to unravel the latent structure for three prominent ...
To cope with these challenges, the present paper brings forth a novel rank-regularized maximum likelihood approach that models categorical data as quantized ...
Scalable online learning adaptive to unknown dynamics. Online subspace learning for big categorical data with misses; Online functional approximation in ...
A novel online (adaptive) algorithm is developed to obtain multi-way decompositions of low-rank tensors with missing entries and perform imputation as a ...
Mardani, and G. B. Giannakis, "Online Categorical Subspace Learning for Sketching Big Data with Misses," IEEE Transactions on Signal Processing, vol. 65, no ...