Multi-View Embedding Learning for Incompletely Labeled Data

Abstract

In many applications, the data may be high dimensional, represented by multiple features, and associated with more than one labels. Embedding learning is an effective strategy for dimensionality reduction and for nearest neighbor search in massive datasets. We propose a novel method to seek compact embedding that allows efficient retrieval with incompletely-labeled multi-view data. Based on multi-graph Laplacian, we achieve the optimal combination of heterogeneous features to effectively describe data, which exploits the feature correlations between different views. We learn the embedding that preserves the neighborhood context in the original spaces, and obtain the complete labels simultaneously. Inter-label correlations are sufficiently leveraged in the proposed framework. Our goal is to find the maps from multiple input spaces to the compact embedding space and to the semantic concept space at the same time. There is semantic gap between the input multi-view feature spaces and the semantic concept space; and the compact embedding space can be looked on as the bridge between the above spaces. Experimental evaluation on three real-world datasets demonstrates the effectiveness of the proposed method.