Abstract-Real world data are often acquired as a collection of matrices rather than as a single matrix. Such multi-block data are naturally linked and typically share some common features and at the same time exhibit their own individual features, reflecting the background in which they are measured and collected. To exploit the linked nature of data, we propose a new framework for common and individual feature extraction (CIFE) which identifies and separates the common and individual features from multi-block data. Two efficient algorithms termed common orthogonal basis extraction (COBE) are proposed to extract the common basis which is shared by all data, independent on whether the number of common components is given or not. Feature extraction is then performed on the common and the individual subspaces separately, by incorporating dimensionality reduction and blind source separation techniques. Extensive experimental results on both synthetic and real-world data show significant advantages of the proposed CIFE method in comparison to the state-of-the-art.
Inspired by JIVE, Zhou et al. REF used a common orthogonal basis extraction (COBE) algorithm to identify and separate the shared and individual features.
Group Component Analysis for Multiblock Data: Common and Individual Feature Extraction
IEEE Transactions on Neural Networks and Learning Systems, Volume: 27, Issue: 11, Nov. 2016
Computer Science Medicine