Enhanced canonical correlation analysis with local density for cross-domain visual classification

Abstract

Real-world visual classification tasks typically need to deal with data observed from different domains. Inspired by canonical correlation analysis (CCA), we propose an enhanced CCA with local density for associating and recognizing cross-domain data. In addition to maximizing the correlation of the projected cross-domain data, our CCA model further exploits the local density information observed from each domain. As a result, our CCA not only exhibits excellent abilities in identifying representative data, noisy data like outliers can be further suppressed during the derivation of our CCA subspace. In our experiments, we successfully apply the proposed methods for solving two cross-domain classification tasks: person re-identification and cross-view action recognition.