1. Experimental Perspectives on Lasso-related Algorithms on Parallel Computing Frameworks Jichuan Zeng

2. Experimental Perspectives on Lasso-related Algorithms on Parallel Computing Frameworks
Big Data era (4V, Volume, Variety, Variability, Velocity)
Still lacks comparison of the state of art Big Data frameworks in specific problems in large-scale dataset.
Lasso-related algorithm
large-scale, sparsity and slow convergence
What are main features and differences of these distributed ML frameworks?
Can distributed ML frameworks above capable of solving the lasso-related optimization problem on huge-scale data sets?
What is the trade-off between the performance of frameworks and the sparsity of data?
What is main factor in each framework that retards the lasso-related algorithms when the scale of data soars?

3. Distributed Machine Learning Frameworks
Graphlab
- Graph-based
Petuum
Parameter Server
Stale Synchronous Parallel
Spark
General-purpose
Resilient Distributed Datasets

4. Lasso on ML Frameworks
Environment
Arcane
Multi-core servers, running VMware. For each virtual machine, we configured 4 cores(2.5 GHz each) and 16 GB of RAM
Dataset
Arcene
The sparser dataset contains 10K features and 54K non-zero entries
Graphlab performs poor in shared variables applications compared to the Petuum and Spark which deploy on master/workers mode

5. Future Works More lasso-related models
Group lasso
Elastic net
Fused lasso
Graphical lasso
Try to improve the current distributed ML frameworks
Load balance
Heuristic update