Abstract
Despite the advances in semi-supervised learning methods, these algorithms face three limitations. The first is the assumption of pre-constructed graphs and the second is their inability to process large databases. The third limitation is that these methods ignore the topological imbalance of the data in a graph. In this paper, we address these limitations and propose a new approach called Weighted Simultaneous Graph Construction and Reduced Flexible Manifold Embedding (W-SGRFME). To overcome the first limitation, we construct the affinity graph using an automatic algorithm within the learning process. The second limitation concerns the ability of the model to handle a large number of unlabeled samples. To this end, the anchors are included in the algorithm as data representatives, and an inductive algorithm is used to estimate the labeling of a large number of unseen samples. To address the topological imbalance of the data samples, we introduced the Renode method to assign weights to the labeled samples. We evaluate the effectiveness of the proposed method through experimental results on two large datasets commonly used in semi-supervised learning: Covtype and MNIST. The results demonstrate the superiority of the W-SGRFME method over two recently proposed models and emphasize its effectiveness in dealing with large datasets.
Zoulfikar Ibrahim
Professor and Software Engineer
I develop scalable graph-based machine learning methods and teach cutting-edge technologies in web and software development. My focus spans semi-supervised learning, data analysis, and full-stack engineering.
Fadi Dornaika
Ikerbasque Research Professor
Ikerbasque Research Professor with expertise in computer vision, machine learning, and pattern recognition.