  Due to the rapid advancements of sensory and computing technology,
multi-modal data sources that represent the same pattern or phenomenon have
attracted growing attention. As a result, finding means to explore useful
information from these multi-modal data sources has quickly become a necessity.
In this paper, a discriminative vectorial framework is proposed for multi-modal
feature representation in knowledge discovery by employing multi-modal hashing
(MH) and discriminative correlation maximization (DCM) analysis. Specifically,
the proposed framework is capable of minimizing the semantic similarity among
different modalities by MH and exacting intrinsic discriminative
representations across multiple data sources by DCM analysis jointly, enabling
a novel vectorial framework of multi-modal feature representation. Moreover,
the proposed feature representation strategy is analyzed and further optimized
based on canonical and non-canonical cases, respectively. Consequently, the
generated feature representation leads to effective utilization of the input
data sources of high quality, producing improved, sometimes quite impressive,
results in various applications. The effectiveness and generality of the
proposed framework are demonstrated by utilizing classical features and deep
neural network (DNN) based features with applications to image and multimedia
analysis and recognition tasks, including data visualization, face recognition,
object recognition; cross-modal (text-image) recognition and audio emotion
recognition. Experimental results show that the proposed solutions are superior
to state-of-the-art statistical machine learning (SML) and DNN algorithms.
