… , we will briefly introduce multi-label learning and dimensionality reduction, including existing
… challenges of multi-label dimensionality reduction. 1.2 Applications of Multi-Label Learning …

… the enlarged multi-label information to learn a transformation matrix for dimensionality reduction.
… hence obtaining a better subspace for dimensionality reduction. The rest of this paper is …

… Abstract—Multi-label classification has gained in importance … Therefore, dimensionality
reduction, which aims at reducing … presenting dimensionality reduction approaches for multi-label …

… analyze dimensionality reduction in the context of multi-label … in which we perform
dimensionality reduction and multi-label … dimensionality reduction step followed by multi-label …

… multi-label dimensionality reduction via dependence maximization (MDDM) and multi-label
linear … correlation between tags, and ELM is also a good choice for multi-label classification. …

… Label space dimension reduction (LSDR) is an efficient and effective paradigm for multi-label
classification with many classes. Existing approaches to LSDR, such as compressive …

… ) and each high-dimensional sample is associated with … -supervised and multi-label
dimensionality reduction method … Noisy multi-label semi-supervised dimensionality reduction (…

… Our aim is to solve dimension reduction and multi-label inference simultaneously. For
this task, a reasonable choice of cost function is reconstruction error (Roweis and Saul 2000), …

… To mitigate the issues suffered by these semi-supervised multi-label dimensionality
reduction methods, we introduce the SMDRdm approach. Different from the dependence term …

… data, multi-label dimensionality reduction remains almost untouched. In this paper, we … a
multi-label dimensionality reduction method called MDDM (Multi-label Dimensionality reduction …