Heterogeneous Information Network (HIN) has attracted much attention due to its wide applicability in a variety of data mining tasks, especially for tasks with multi-typed objects. A potentially large number of meta-paths can be extracted from the heterogeneous networks, providing abundant semantic knowledge. Though a variety of meta-paths can be defined, too many meta-paths are redundant. Reduction on the number of meta-paths can enhance the effectiveness since some redundant meta-paths provide interferential linkage to the task. Moreover, the reduced meta-paths can reflect the characteristic of the heterogeneous network. Previous endeavors try to reduce the number of meta-paths under the guidance of supervision information. Nevertheless, supervised information is expensive and may not always be available. In this paper, we propose a novel algorithm, SPMR (Semantic Preserving Meta-path Reduction), to reduce a set of pre-defined meta-paths in an unsupervised setting. The proposed method is able to evaluate a set of meta-paths to maximally preserve the semantics of original meta-paths after reduction. Experimental results show that SPMR can select a succinct subset of meta-paths which can achieve comparable or even better performance with fewer meta-paths.
In unsupervised feature selection, different heuristics have been explored for selecting features. Selecting features by their spectral property is a popular class of approaches @cite_7 @cite_22 . However, these simple heuristics can only evaluate features individually and ignore the correlation among features. Recent methods attempt to overcome this issue by evaluating the subset of features as a whole. Notably, @math norm based methods @cite_14 @cite_24 @cite_8 @cite_19 have gained much popularity among others. The feature selection problem is performed jointly with linear subspace learning linear regression. In such methods, the features are evaluated by their utility in the regression problem. Sparsity-inducing @math norm is employed to enforce the weights of less useful features shrink to zero. For example, Non-negative Discriminative Feature Selection (NDFS) @cite_24 performs non-negative spectral analysis and feature selection jointly. Robust Unsupervised Feature Selection (RUFS) @cite_8 and Robust Spectral Feature Selection (RSFS) @cite_4 study feature selection robust to outlier instances by using @math norm and Huber loss, respectively. FSASL @cite_19 employs adaptive structure learning to be more resilient to the noise in the local structure. However, these approaches can only be applied to feature vectors and is not applicable to meta-path selection.
@cite_14 @cite_4 @cite_22 @cite_7 @cite_8 @cite_24 @cite_19
2084376194 141062567 2083666679 120692785
In this paper, we consider the problem of unsupervised feature selection. Recently, spectral feature selection algorithms, which leverage both graph Laplacian and spectral regression, have received increasing attention. However, existing spectral feature selection algorithms suffer from two major problems: 1) since the graph Laplacian is constructed from the original feature space, noisy and irrelevant features may have adverse effect on the estimated graph Laplacian and hence degenerate the quality of the induced graph embedding, 2) since the cluster labels are discrete in natural, relaxing and approximating these labels into a continuous embedding can inevitably introduce noise into the estimated cluster labels. Without considering the noise in the cluster labels, the feature selection process may be misguided. In this paper, we propose a Robust Spectral learning framework for unsupervised Feature Selection (RSFS), which jointly improves the robustness of graph embedding and sparse spectral regression. Compared with existing methods which are sensitive to noisy features, our proposed method utilizes a robust local learning method to construct the graph Laplacian and a robust spectral regression method to handle the noise on the learned cluster labels. In order to solve the proposed optimization problem, an efficient iterative algorithm is proposed. We also show the close connection between the proposed robust spectral regression and robust Huber M-estimator. Experimental results on different datasets show the superiority of RSFS. In this paper, a new unsupervised learning algorithm, namely Nonnegative Discriminative Feature Selection (NDFS), is proposed. To exploit the discriminative information in unsupervised scenarios, we perform spectral clustering to learn the cluster labels of the input samples, during which the feature selection is performed simultaneously. The joint learning of the cluster labels and feature selection matrix enables NDFS to select the most discriminative features. To learn more accurate cluster labels, a nonnegative constraint is explicitly imposed to the class indicators. To reduce the redundant or even noisy features, l2,1-norm minimization constraint is added into the objective function, which guarantees the feature selection matrix sparse in rows. Our algorithm exploits the discriminative information and feature correlation simultaneously to select a better feature subset. A simple yet efficient iterative algorithm is designed to optimize the proposed objective function. Experimental results on different real world datasets demonstrate the encouraging performance of our algorithm over the state-of-the-arts. By removing the irrelevant and redundant features, feature selection aims to find a compact representation of the original feature with good generalization ability. With the prevalence of unlabeled data, unsupervised feature selection has shown to be effective in alleviating the curse of dimensionality, and is essential for comprehensive analysis and understanding of myriads of unlabeled high dimensional data. Motivated by the success of low-rank representation in subspace clustering, we propose a regularized self-representation (RSR) model for unsupervised feature selection, where each feature can be represented as the linear combination of its relevant features. By using L 2 , 1 -norm to characterize the representation coefficient matrix and the representation residual matrix, RSR is effective to select representative features and ensure the robustness to outliers. If a feature is important, then it will participate in the representation of most of other features, leading to a significant row of representation coefficients, and vice versa. Experimental analysis on synthetic and real-world data demonstrates that the proposed method can effectively identify the representative features, outperforming many state-of-the-art unsupervised feature selection methods in terms of clustering accuracy, redundancy reduction and classification accuracy. HighlightsA regularized self-representation (RSR) model is proposed for unsupervised feature selection.An iterative reweighted least-squares algorithm is proposed to solve the RSR model.The proposed method shows superior performance to state-of-the-art. A new unsupervised feature selection method, i.e., Robust Unsupervised Feature Selection (RUFS), is proposed. Unlike traditional unsupervised feature selection methods, pseudo cluster labels are learned via local learning regularized robust nonnegative matrix factorization. During the label learning process, feature selection is performed simultaneously by robust joint l2,1 norms minimization. Since RUFS utilizes l2,1 norm minimization on processes of both label learning and feature learning, outliers and noise could be effectively handled and redundant or noisy features could be effectively reduced. Our method adopts the advantages of robust non-negative matrix factorization, local learning, and robust feature learning. In order to make RUFS be scalable, we design a (projected) limited-memory BFGS based iterative algorithm to efficiently solve the optimization problem of RUFS in terms of both memory consumption and computation complexity. Experimental results on different benchmark real world datasets show the promising performance of RUFS over the state-of-the-arts.