1 code implementation • 17 Mar 2024 • Antonio M. Sudoso
Biclustering, also called co-clustering, block clustering, or two-way clustering, involves the simultaneous clustering of both the rows and columns of a data matrix into distinct groups, such that the rows and columns within a group display similar patterns.
1 code implementation • 15 Dec 2023 • Veronica Piccialli, Jan Schwiddessen, Antonio M. Sudoso
For the upper bound, instead, we define a local search exploiting the solution of the SDP relaxation.
1 code implementation • 31 Oct 2023 • Giovanni Felici, Antonio M. Sudoso
In essence, it incorporates an additional learning and optimization task into the standard feature-based forecasting approach, focusing on the identification of an optimal set of forecasting methods.
no code implementations • 11 Feb 2023 • Dario Piermarini, Antonio M. Sudoso, Veronica Piccialli
Predicting financial distress in municipalities can be a complex task, as it involves understanding a wide range of factors that can affect a municipality's financial health.
1 code implementation • 19 Sep 2022 • Veronica Piccialli, Antonio M. Sudoso
In this paper, we propose a global optimization approach based on the branch-and-cut technique to solve the cardinality-constrained MSSC.
1 code implementation • 30 Nov 2021 • Veronica Piccialli, Anna Russo Russo, Antonio M. Sudoso
The minimum sum-of-squares clustering (MSSC), or k-means type clustering, is traditionally considered an unsupervised learning task.
no code implementations • 31 May 2020 • Paolo Mancuso, Veronica Piccialli, Antonio M. Sudoso
In this paper, we propose a machine learning approach for forecasting hierarchical time series.
3 code implementations • 15 Nov 2019 • Veronica Piccialli, Antonio M. Sudoso
Energy disaggregation, known in the literature as Non-Intrusive Load Monitoring (NILM), is the task of inferring the power demand of the individual appliances given the aggregate power demand recorded by a single smart meter which monitors multiple appliances.