Online selection of dynamic features has attracted intensive interest in recent years. However, existing online feature selection methods evaluate features individually and ignore the underlying structure of a feature stream. For instance, in image analysis, features are generated in groups which represent color, texture, and other visual information. Simply breaking the group structure in feature selection may degrade performance. Motivated by this observation, we formulate the problem as an online group feature selection. The problem assumes that features are generated individually but there are group structures in the feature stream. To the best of our knowledge, this is the first time that the correlation among streaming features has been considered in the online feature selection process. To solve this problem, we develop a novel online group feature selection method named OGFS. Our proposed approach consists of two stages: online intra-group selection and online inter-group selection. In the intra-group selection, we design a criterion based on spectral analysis to select discriminative features in each group. In the inter-group selection, we utilize a linear regression model to select an optimal subset. This two-stage procedure continues until there are no more features arriving or some predefined stopping conditions are met. Finally, we apply our method to multiple tasks including image classification and face verification. Extensive empirical studies performed on real-world and benchmark data sets demonstrate that our method outperforms other state-of-the-art online feature selection methods.
Generally, the feature selection methods fall into three classes based on how the label information is used. Most existing methods are supervised which evaluate the correlation among features and the label variable. Due to the difficulty in obtaining labeled data, unsupervised feature selection has attracted increasing attention in recent years @cite_40 . Unsupervised feature selection methods usually select features that preserve the data similarity or manifold structure @cite_50 . Semi-supervised feature selection, so called small-labeled sample problem'', makes use of label information and manifold structure corresponding to labeled data and unlabeled data @cite_34 .
@cite_40 @cite_34 @cite_50
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Compared with supervised learning for feature selection, it is much more difficult to select the discriminative features in unsupervised learning due to the lack of label information. Traditional unsupervised feature selection algorithms usually select the features which best preserve the data distribution, e.g., manifold structure, of the whole feature set. Under the assumption that the class label of input data can be predicted by a linear classifier, we incorporate discriminative analysis and l2,1-norm minimization into a joint framework for unsupervised feature selection. Different from existing unsupervised feature selection algorithms, our algorithm selects the most discriminative feature subset from the whole feature set in batch mode. Extensive experiment on different data types demonstrates the effectiveness of our algorithm. Each year worldwide, more and more data is collected. In fact, it is estimated that the amount of data collected and stored at least doubles every 2years. Of this data, a large percentage is unlabelled or has labels which are incomplete or missing. It is because this data is so large that it becomes very difficult for humans to manually assign labels to data objects. Additionally, many real-world application datasets such as those in gene expression analysis, and text classification are also of large dimensionality. This further frustrates the process of label assignment for domain experts as not all of the features are relevant or necessary in order to assign a given label. Hence unsupervised feature selection is required. For supervised learning, feature selection algorithms attempt to maximise a given function of predictive accuracy. This function typically considers the ability of feature vectors to reflect decision class labels. However, for the unsupervised learning task, decision class labels are not provided, which poses questions such as: which features should be retained? In fact, not all features are important and some are irrelevant, redundant or noisy. In this paper, several unsupervised FS approaches are presented which are based on fuzzy-rough sets. These approaches require no thresholding information, are domain-independent, and can operate on real-valued data without the need for discretisation. They offer a significant reduction in dimensionality whilst retaining the semantics of the data, and can even result in supersets of the supervised fuzzy-rough approaches. The approaches are compared with some supervised techniques and are shown to retain useful features. In supervised learning scenarios, feature selection has been studied widely in the literature. Selecting features in unsupervised learning scenarios is a much harder problem, due to the absence of class labels that would guide the search for relevant information. And, almost all of previous unsupervised feature selection methods are "wrapper" techniques that require a learning algorithm to evaluate the candidate feature subsets. In this paper, we propose a "filter" method for feature selection which is independent of any learning algorithm. Our method can be performed in either supervised or unsupervised fashion. The proposed method is based on the observation that, in many real world classification problems, data from the same class are often close to each other. The importance of a feature is evaluated by its power of locality preserving, or, Laplacian Score. We compare our method with data variance (unsupervised) and Fisher score (supervised) on two data sets. Experimental results demonstrate the effectiveness and efficiency of our algorithm. Feature selection aims to reduce dimensionality for building comprehensible learning models with good generalization performance. Feature selection algorithms are largely studied separately according to the type of learning: supervised or unsupervised. This work exploits intrinsic properties underlying supervised and unsupervised feature selection algorithms, and proposes a unified framework for feature selection based on spectral graph theory. The proposed framework is able to generate families of algorithms for both supervised and unsupervised feature selection. And we show that existing powerful algorithms such as ReliefF (supervised) and Laplacian Score (unsupervised) are special cases of the proposed framework. To the best of our knowledge, this work is the first attempt to unify supervised and unsupervised feature selection, and enable their joint study under a general framework. Experiments demonstrated the efficacy of the novel algorithms derived from the framework.