The molecular mechanisms and functions in complex biological systems currently remain
elusive. Recent high-throughput techniques, such as next-generation sequencing, have …

Incomplete multi-view clustering partitions multi-view data suffering from missing views, for
which matrix factorization approaches seek the latent representation of incomplete multi …

Multi-view clustering is an important approach for analyzing multi-view data in an
unsupervised way. Among various methods, the multi-view subspace clustering approach …

Multi-view feature selection aims at obtaining a subset of informative features from
heterogeneous feature domains. Recent graph based approaches mostly learn view …

As one category of important incomplete multi-view clustering methods, subspace based
methods seek the common latent representation of incomplete multi-view data by matrix …

The various variants of the classical least square regression (LSR) have been extensively
utilized in numerous applications. However, most previous linear regression methods only …

With the expansion of renewable energy sources and its integration with the grid, power
semiconductor devices applications has been increased rapidly which causes many Power …

Palmprint recognition has been widely used in security authentication. However, most of the
existing palmprint representation methods are focused on a special application scenario …

Multi-view representation learning is a promising and challenging research topic, which
aims to integrate multiple data information from different views to improve the learning …

Deep neural networks and Multiple Kernel Learning are representation learning
methodologies of widespread use and increasing success. While the former aims at learning …