Scalable Semi-Supervised Kernel Spectral Learning using Random Fourier Features

We live in the era of big data with dataset sizes growing steadily over the past decades. In addition, obtaining expert labels for all the instances is time-consuming and in many cases may not even be possible. This necessitates the development of advanced semi-supervised models that can learn from both labeled and unlabeled data points and also scale at worst linearly with the number of examples. In the context of kernel based semi-supervised models, constructing the training kernel matrix for the large training dataset is expensive and memory inefficient. This paper investigates the scalability of the recently proposed multiclass semi-supervised kernel spectral clustering model (MSSKSC) by means of random Fourier features. The proposed model maps the input data into an explicit low-dimensional feature space. Thanks to the explicit feature maps, one can then solve the MSSKSC optimization formation in the primal, making the complexity of the method linear in number of training data points. The performance of the proposed model is compared with that of recently introduced reduced kernel techniques and Nystrom based MSSKSC approaches. Experimental results demonstrate the scalability, efficiency and faster training computation times of the proposed model over conventional large scale semi-supervised models on large scale real-life datasets.