In multi-dimensional classification (MDC), the semantics of objects are characterized by multiple class variables from different dimensions. Existing MDC approaches focus on designing effective class dependency modeling strategies to enhance classification performance. However, the intercoupling of multiple class variables poses a significant challenge to the precise modeling of class dependencies. In this paper, we make the first attempt towards escaping from class dependency modeling for addressing MDC problems. Accordingly, a novel MDC approach named DCOM is proposed by decoupling the interactions of different dimensions in MDC. Specifically, DCOM endeavors to identify a latent factor that encapsulates the most salient and critical feature information. This factor will facilitate partial conditional independence among class variables conditioned on both the original feature vector and the learned latent embedding. Once the conditional independence is established, classification models can be readily induced by employing simple neural networks on each dimension. Extensive experiments conducted on benchmark data sets demonstrate that DCOM outperforms other state-of-the-art MDC approaches.

In real-world scenarios, objects usually need labels across multiple dimensions. For example, a landscape picture can be labeled from time dimension (with possible labels "morning", "afternoon", and "night", etc.), from weather dimension (with possible labels "sunny", "rainy", "cloudy", etc.), and from scene dimension (with possible labels "desert", "mountain", "grass", etc.).These labels can interact in complex ways --- label "sunny" in weather dimension can not occur with label "night" in time dimension while label "dessert" in scene dimension is often accompanied by "sunny".Such intricate dependencies make Multi-Dimensional Classification (MDC), a task requiring predictions across multiple class spaces particularly challenging. This paper introduces a groundbreaking approach called DCOM, which bypasses the need to directly analyze class dependencies.Instead, DCOM learns a latent factor to capture the most essential and critical feature information to naturally untangles the chaos caused by interrelated dimensions. This allows simple, independent models to handle each dimension efficiently, like using basic neural networks for separate tasks.As a first attempt towards escaping from class dependency modeling in MDC, DCOM achieves state-of-the-art performance while marking a paradigm shift in handling complex class interactions.