The task of gait-based subject recognition (SR) in ubiquitous sensor environments has become popular due to its wide range of applications in biometric authentication and smart home products. In recent times, a significant amount of work has been done in SR using supervised learning algorithms on datasets having high modality. However, the process of annotation for SR is difficult due to the challenges like privacy and high manual cost, which results in a scarcity of labeled data samples. Also, for the datasets having less modality, the task of SR in a semi-supervised domain is sparsely explored and challenging. In this work, we analyze the effect of these two factors (sparse labels and low modality) which are critical for SR in ubiquitous data. We select two datasets of ubiquitous data that are relatively unexplored in the context of SR. The datasets, namely OPPOR-TUNITY and Smartphone used to perform SR using conventional supervised learning algorithms to benchmark the results. Then we perform extensive experimentation to analyze the effect of the aforementioned factors over the task of SR by studying the variations in classification accuracies. Next, we propose our semi-supervised framework for SR based on the concept of pseudo labels to counter the adverse effects of low modality and lack of labels. Experimental results show that our approach offers up to 77% and 98% accuracy on the Smartphone and OPPORTUNITY dataset respectively. © 2020 IEEE.