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Found 5 papers, 0 papers with code
Dynamic Subspace Estimation with Grassmannian Geodesics
We propose a novel algorithm for minimizing this objective and estimating the parameters of the model from data with Grassmannian-constrained optimization.
Scaling-up Distributed Processing of Data Streams for Machine Learning
This paper reviews recently developed methods that focus on large-scale distributed stochastic optimization in the compute- and bandwidth-limited regime, with an emphasis on convergence analysis that explicitly accounts for the mismatch between computation, communication and streaming rates.
Distributed Stochastic Algorithms for High-rate Streaming Principal Component Analysis
The analysis of DM-Krasulina shows that it can also achieve order-optimal estimation error rates under appropriate conditions, even when some samples have to be discarded within the network due to communication latency.
Tensor Regression Using Low-rank and Sparse Tucker Decompositions
It focuses on the task of estimating the regression tensor from $m$ realizations of the response variable and the predictors where $m\ll n = \prod \nolimits_{i} n_i$.
Cloud K-SVD: A Collaborative Dictionary Learning Algorithm for Big, Distributed Data
In contrast to previous works on subspace-based data representations, this paper focuses on the geometric structure of a union of subspaces (UoS).
