# An Effective Multi-Label Feature Selection Model Towards Eliminating Noisy Features
## 2 Related Work  
Feature selection approaches are commonly specified to a certain recognition scenario, i.e., single-label learning or multi-label learning, because of the different concerns of the two recognition tasks. The issue of noisy feature elimination is firstly raised in single-label feature selection, focusing on removing irrelevant features and picking out discriminative ones. For example, the popular single-label feature selection family by preserving instance similarity [20] directly highly scores the most discriminative features under various statistical metrics, such as the Laplacian score [7; 21], the Fisher score [6], the Hilbert-Schmidt independence criterion [22], and the trace ratio [23], just to name a few. In addition to the above similarity preservation approaches, some traditional distance or instance difference based ones can also be deemed as simply pursuing \"target-specific features,\" such as Relieff [10], SPEC [24; 25], and SPFS [20]. This denotation arises from the fact that target-specific features are picked based only on whether they are strongly correlated with the learning targets. In other words, those features that have excellent discriminative abilities for targets will prevail. The aforementioned approaches have generally achieved excellent performance in eliminatingirrelevant features, while may experience difficulties in improving learning performance due to their scarce attention on removing redundant features. Recently, some remarkable neural networks-based and fuzzy logic-based feature selection works have been presented, which have received extensive attention due to their excellent feature selection performances [26; 27; 28]. For example, [PERSON] and [PERSON] [26] proposed a feedforward neural network-based approach to find the salient features and remove those yielding the least accurate classifications. [PERSON] et al. [27] highly scored the features most related to the drowsiness level via an adaptive neuro-fuzzy inference system, which was devised by combining filter and wrapper feature selection approaches. [PERSON] et al. [28] selected the mostly relevant features for better binary classification by combining several filter approaches through a fuzzy inference system. Generally speaking, the above studies serve as excellent examples of picking out target-specific features, while still leaving aside the underlying negative effects of noisy features. A salient but redundant feature provides little valuable learning information if selected. Although this issue is ignored by a majority of feature selection approaches, it gains attention from some information-based ones. Among them, the family based on mutual information is regarded as the mainstream redundancy removing approach. The classical mutual information [9] and its variants (e.g., conditional mutual information) [5; 29] can effectively position the redundant features and remove them via a greedy search. Nevertheless, an inevitable problem is that the performances of these approaches heavily depend on their probability estimation accuracy. This problem is more complicated in high-dimensional space. In terms of multi-label feature selection approaches, they can be roughly categorized into two families. The first family directly divides the multi-label learning into multiple subproblems and utilizes single-label feature evaluation metrics to tackle them [4]. For instance, [PERSON] is tailed for multi-label learning by dividing its estimations of nearest misses and hits to eight subproblems [30]. In addition, some single-label feature evaluation strategies are also reformulated to the multi-label ones by enforcing on each subgroup, such as class separability and linear discriminant analysis [31; 32]. A major drawback of the above subproblem division strategy is that it ignores label correlations, which encode the underlying label structures for recognition and play critical roles in multi-label learning.