Inductive Semi-supervised Learning Through Optimal Transport
@inproceedings{Hamri2021InductiveSL, title={Inductive Semi-supervised Learning Through Optimal Transport}, author={Mourad El Hamri and Youn{\`e}s Bennani and Issam Falih}, booktitle={International Conference on Neural Information Processing}, year={2021} }
In this paper, we tackle the inductive semi-supervised learning problem that aims to obtain label predictions for out-of-sample data. The proposed approach, called Optimal Transport Induction (OTI), extends efficiently an optimal transport based transductive algorithm (OTP) to inductive tasks for both binary and multi-class settings. A series of experiments are conducted on several datasets in order to compare the proposed approach with state-of-the-art methods. Experiments demonstrate the…
One Citation
Incremental Unsupervised Domain Adaptation Through Optimal Transport
- Computer Science2022 International Joint Conference on Neural Networks (IJCNN)
- 2022
The proposed approach, called DA-OTP, aims to learn a gradual subspace alignment of the source and target domains through Supervised Locality Preserving Projection, so that projected data in the joint low-dimensional latent subspace can be domain-invariant and easily separable.
References
SHOWING 1-10 OF 17 REFERENCES
Label Propagation Through Optimal Transport
- Computer Science2021 International Joint Conference on Neural Networks (IJCNN)
- 2021
The proposed approach, Optimal Transport Propagation (OTP), performs in an incremental process, label propagation through the edges of a complete bipartite edge-weighted graph, whose affinity matrix is constructed from the optimal transport plan between empirical measures defined on labeled and unlabeled data.
Efficient Non-Parametric Function Induction in Semi-Supervised Learning
- Computer ScienceAISTATS
- 2005
Experiments show that the proposed non-parametric algorithms which provide an estimated continuous label for the given unlabeled examples are extended to function induction algorithms that correspond to the minimization of a regularization criterion applied to an out-of-sample example, and happens to have the form of a Parzen windows regressor.
Label Propagation through Linear Neighborhoods
- Computer ScienceIEEE Transactions on Knowledge and Data Engineering
- 2008
A novel graph-based semi supervised learning approach is proposed based on a linear neighborhood model, which assumes that each data point can be linearly reconstructed from its neighborhood, and can propagate the labels from the labeled points to the whole data set using these linear neighborhoods with sufficient smoothness.
Graph-based semi-supervised learning
- Computer ScienceArtificial Life and Robotics
- 2009
First, a method called linear neighborhood propagation is proposed, which can automatically construct the optimal graph, and a novel multilevel scheme is introduced to make the algorithm scalable for large data sets.
Learning from labeled and unlabeled data with label propagation
- Computer Science
- 2002
A simple iterative algorithm to propagate labels through the dataset along high density are as d fined by unlabeled data is proposed and its solution is analyzed, and its connection to several other algorithms is analyzed.
Label Propagation and Quadratic Criterion
- Computer Science
- 2006
This chapter shows how different graph-based algorithms for semi-supervised learning can be cast into a common framework where one minimizes a quadratic cost criterion whose closed-form solution is found by solving a linear system of size n.
An Information-Theoretic External Cluster-Validity Measure
- Computer ScienceUAI
- 2002
In this paper we propose a measure of clustering quality or accuracy that is appropriate in situations where it is desirable to evaluate a clustering algorithm by somehow comparing the clusters it…
Computational Optimal Transport: With Applications to Data Science
- Computer Science
- 2019
Computational Optimal Transport presents an overview of the main theoretical insights that support the practical effectiveness of OT before explaining how to turn these insights into fast computational schemes.
Sinkhorn Distances: Lightspeed Computation of Optimal Transport
- Computer ScienceNIPS
- 2013
This work smooths the classic optimal transport problem with an entropic regularization term, and shows that the resulting optimum is also a distance which can be computed through Sinkhorn's matrix scaling algorithm at a speed that is several orders of magnitude faster than that of transport solvers.
On the Translocation of Masses
- Mathematics
- 2006
The original paper was published in Dokl. Akad. Nauk SSSR, 37, No. 7–8, 227–229 (1942). We assume that R is a compact metric space, though some of the definitions and results given below can be…