Weak Monotonicity With Trend Analysis for Unsupervised Feature Evaluation.

In many real-world unsupervised learning applications, given data with balanced distribution, that is, there are an approximately equal number of instances in each class, we often need to construct a model to reveal such balance. However, in many data, especially the high-dimensional ones, the data in the original feature space often do not present such balance due to the redundant and noisy features. To tackle this problem, we apply an unsupervised spectral feature selection method to select some informative features, which can better reveal the balanced structure of data. Although spectral feature selection is one of the most popular unsupervised feature selection methods and has been widely studied, none of the existing spectral feature selection methods consider the balance property of data. To address this issue, in this article, we propose a novel balanced spectral feature selection (BSFS) method, which not only selects the discriminative features but also picks those to reveal the balanced structure of data. To the best of our knowledge, this is the first spectral feature selection method considering balance structure of data. By introducing a balanced regularization term, we integrate the balanced spectral clustering and feature selection into a unified framework seamlessly. At last, the experiments on benchmark datasets show that the proposed one outperforms the conventional feature selection methods in both clustering performance and balance, which demonstrates the effectiveness and efficiency of the proposed method.

Graph self-representation method for unsupervised feature selection

Subspace distance is an invaluable tool exploited in a wide range of feature selection methods. The power of subspace distance is that it can identify a representative subspace, including a group of features that can efficiently approximate the space of original features. On the other hand, employing intrinsic statistical information of data can play a significant role in a feature selection process. Nevertheless, most of the existing feature selection methods founded on the subspace distance are limited in properly fulfilling this objective. To pursue this void, we propose a framework that takes a subspace distance into account which is called “Variance–Covariance subspace distance”. The approach gains advantages from the correlation of information included in the features of data, thus determines all the feature subsets whose corresponding Variance–Covariance matrix has the minimum norm property. Consequently, a novel, yet efficient unsupervised feature selection framework is introduced based on the Variance–Covariance distance to handle both the dimensionality reduction and subspace learning tasks. The proposed framework has the ability to exclude those features that have the least variance from the original feature set. Moreover, an efficient update algorithm is provided along with its associated convergence analysis to solve the optimization side of the proposed approach. An extensive number of experiments on nine benchmark datasets are also conducted to assess the performance of our method from which the results demonstrate its superiority over a variety of state-of-the-art unsupervised feature selection methods. The source code is available at https://github.com/SaeedKarami/VCSDFS.

Feature selection plays a crucial role in scientific research and practical applications. In the real world applications, labeling data is time and labor consuming. Thus, unsupervised feature selection methods are desired for many practical applications. Linear discriminant analysis (LDA) with trace ratio criterion is a supervised dimensionality reduction method that has shown good performance to improve classifications. In this paper, we first propose a unified objective to seamlessly accommodate trace ratio formulation and K-means clustering procedure, such that the trace ratio criterion is extended to unsupervised model. After that, we propose a novel unsupervised feature selection method by integrating unsupervised trace ratio formulation and structured sparsity-inducing norms regularization. The proposed method can harness the discriminant power of trace ratio criterion, thus it tends to select discriminative features. Meanwhile, we also provide two important theorems to guarantee the unsupervised feature selection process. Empirical results on four benchmark data sets show that the proposed method outperforms other sate-of-the-art unsupervised feature selection algorithms in all three clustering evaluation metrics.

In the past decade, various sparse learning based unsupervised feature selection methods have been developed. However, most existing studies adopt a two-step strategy, i.e., selecting the top-m features according to a calculated descending order and then performing K-means clustering, resulting in a group of sub-optimal features. To address this problem, we propose a Dependence Guided Unsupervised Feature Selection (DGUFS) method to select features and partition data in a joint manner. Our proposed method enhances the inter-dependence among original data, cluster labels, and selected features. In particular, a projection-free feature selection model is proposed based on l20-norm equality constraints. We utilize the learned cluster labels to fill in the information gap between original data and selected features. Two dependence guided terms are consequently proposed for our model. More specifically, one term increases the dependence of desired cluster labels on original data, while the other term maximizes the dependence of selected features on cluster labels to guide the process of feature selection. Last but not least, an iterative algorithm based on Alternating Direction Method of Multipliers (ADMM) is designed to solve the constrained minimization problem efficiently. Extensive experiments on different datasets consistently demonstrate that our proposed method significantly outperforms state-of-the-art baselines.

Low-rank structure preserving for unsupervised feature selection

Non-convex regularized self-representation for unsupervised feature selection

The rapid development of big data era incurs the generation of huge amount of data day by day in various fields. Due to the large-scale and high-dimensional characteristics of these data, it is often difficult to achieve better decision-making in practical applications. Therefore, an efficient big data analytical method is urgently necessary. For feature engineering, feature selection seems to be an important research topic which is anticipated to select “excellent” features from candidate ones. The implementation of feature selection can not only achieve the purpose of dimensionality reduction, but also improve the computational efficiency and result performance of the model. In many classification tasks, researchers found that data seem to be usually close to each other if they are from the same class; thus, local compactness is of great importance for the evaluation of a feature. Based on this discovery, we propose a fast unsupervised feature selection algorithm, named Compactness Score (CSUFS), to select desired features. To prove the superiority of the proposed algorithm, several public data sets are considered with extensive experiments being performed. The experiments are presented by applying feature subsets selected through several different algorithms to the clustering task. The performance of clustering tasks is indicated by two well-known evaluation metrics, while the efficiency is reflected by the corresponding running time. As demonstrated, our proposed algorithm is more accurate and efficient compared with existing ones.

The traditional learning machines suffer from the curse of dimensionality because of the data explosion in the areas of multi-media, social network, etc. Feature selection is an effective technique to reduce storage burden and time complexity, and improve generalization ability of the learned models. In real-world applications, the data can be collected from different modalities, or described from multi-views as well. Compared with supervised cases, it is more challenging to reduce the feature dimensionality of multi-view data in unsupervised circumstances. The key difficulty with multiview unsupervised feature selection is how to characterize the multi-view relationships. In this paper, we propose a novel method for multi-view unsupervised feature selection by imposing sparsity on both individual features and views. To exploit the complementary information, we also take the view importance into consideration without introducing explicit view weights. Experiments on benchmark datasets show on the proposed algorithm outperforms other unsupervised feature selection methods.

Selecting the informative features from the high dimensional data can improve the performance of the classification and get a deep understanding of the problems. A non-problem related feature contains little information and has little influence on the data distribution. By permuting the feature and calculating the data distribution difference, how much information the feature contains could be measured. In this paper, we propose an unsupervised feature selection method (EUFSPR), which combines the ensemble technique, clustering, permutation and data distribution evaluation techniques to measure the feature importance. Clustering is adopted to get the sample groups and the data distribution is evaluated by the overlapping areas. Eight gene expression microarray datasets are utilized to demonstrate the effectiveness of the proposed method over the unsupervised feature selection methods and supervised feature selection methods.

Feature selection is selecting a subset of optimal features. Feature selection is being used in high dimensional data reduction and it is being used in several applications like medical, image processing, text mining, etc. Several methods were introduced for unsupervised feature selection. Among those methods some are based on filter approach and some are based on wrapper approach. In the existing work, unsupervised feature selection methods using Genetic Algorithm, Particle Swarm Optimization with Relative Reduct, Quick Reduct and Ant Colony Optimization have been introduced. These methods yield better performance for unsupervised feature selection. In this paper we proposed a novel method to select subset of features from unlabeled data using binary bat algorithm with sum of squared error as the fitness function. The proposed method is then tested with various classification algorithms like decision tree, multilayer perceptron, support vector machine and clustering quality measures like sum of squared error. The results show that our proposed method gives more accuracy when compared with other optimization algorithm.

In many real applications, the collected data is usually high dimensionality of feature space and no labels are given, which will result in the “curse of dimensionality” problem and bring the challenge for the feature selection. Although tremendous efforts have been made in unsupervised feature selection, the existing methods neglect the robustness of jointly feature selection and clustering. In this paper, we present a robust simultaneous feature selection and clustering method. By introducing a robust loss function, the unsupervised linear discriminant analysis methods with two different strategies (the ratio trace and the trace ratio) are incorporated into this loss function, which can guarantee the selected features with robustness. Then two novel simultaneous unsupervised feature selection and clustering objective functions are presented by the proposed loss function equipped with $\ell_{2,1}$-norm. Two alternative iterative optimization algorithms are developed to solve these two objective functions. The comparative experimental results conducted on eight UCI datasets show that the proposed methods are better than other competing methods.

Simultaneous positive sequential vectors modeling and unsupervised feature selection via continuous hidden Markov models

Orthogonally constrained matrix factorization for robust unsupervised feature selection with local preserving

Unsupervised feature selection with adaptive multiple graph learning