Selecting feature subset with sparsity and low redundancy for unsupervised learning

et al. 2018

J Image Video Proc.

Feature selection, which aims to select an optimal feature subset to avoid the "curse of dimensionality," is an important research topic in many real-world applications. To select informative features from a highdimensional dataset, we propose a novel unsupervised feature selection algorithm called Double Regularized Matrix Factorization Feature Selection (DRMFFS) in this paper. DRMFFS is based on the feature selection framework of matrix factorization, but extends this framework by introducing double regularizations (i.e., graph regularization and inner product regularization). There are three major contributions to our approach. First, for the sake of preserving the useful underlying geometric structure information of the feature space of the data, we introduce the graph regularization to guide the learning of the feature selection matrix, making it more effective. Second, in order to take into account the correlations among features, an inner product regularization term is imposed on the objective function of matrix factorization. Therefore, the selected features by DRMFFS cannot only represent the original high-dimensional data well but also contain low redundancy. Third, we design an efficient iteratively update algorithm to solve our approach and also prove its convergence. Experiments on six benchmark databases demonstrate that the proposed approach outperforms the state-of-the-art approaches in terms of both the classification and clustering performance.

Section: A Jimentioning

confidence: 99%

Section: A Jimentioning

confidence: 99%

Section: A Jimentioning

confidence: 99%

Section: A Jimentioning

confidence: 99%

See 2 more Smart Citations

Double regularized matrix factorization for image classification and clustering

Zhou

et al. 2018

J Image Video Proc.

“…By choosing a small subset of the most informative features that ideally is necessary and sufficient to describe the target concept [10], feature selection is capable of solving data mining and pattern recognition problems with data sets involving large number of features. Some have attempted to explore the advantages of feature selection for credit risk evaluation for P2P lending.…”

Section: Introductionmentioning

confidence: 99%

P2P Lending Analysis Using the Most Relevant Graph-Based Features

Cui

Bai

Lecture Notes in Computer Science

et al. 2016

Abstract. Peer-to-Peer (P2P) lending is an online platform to facilitate borrowing and investment transactions. A central problem for these P2P platforms is how to identify the most influential factors that are closely related to the credit risks. This problem is inherently complex due to the various forms of risks and the numerous influencing factors involved. Moreover, raw data of P2P lending are often high-dimension, highly correlated and unstable, making the problem more untractable by traditional statistical and machine learning approaches. To address these problems, we develop a novel filter-based feature selection method for P2P lending analysis. Unlike most traditional feature selection methods that use vectorial features, the proposed method is based on graph-based features and thus incorporates the relationships between pairwise feature samples into the feature selection process. Since the graph-based features are by nature completed weighted graphs, we use the steady state random walk to encapsulate the main characteristics of the graph-based features. Specifically, we compute a probability distribution of the walk visiting the vertices. Furthermore, we measure the discriminant power of each graph-based feature with respect to the target feature, through the Jensen-Shannon divergence measure between the probability distributions from the random walks. We select an optimal subset of features based on the most relevant graph-based features, through the Jensen-Shannon divergence measure. Unlike most existing state-of-the-art feature selection methods, the proposed method can accommodate both continuous and discrete target features. Experiments demonstrate the effectiveness and usefulness of the proposed feature selection algorithm on the problem of P2P lending platforms in China.

Minimising Entropy Changes in Dynamic Network Evolution

Graph-Based Representations in Pattern Recognition

Wilson

Hancock

2017

Abstract. In this paper, we propose a novel method to adaptively select the most informative and least redundant feature subset, which has strong discriminating power with respect to the target label. Unlike most traditional methods using vectorial features, our proposed approach is based on graph-based features and thus incorporates the relationships between feature samples into the feature selection process. To efficiently encapsulate the main characteristics of the graphbased features, we probe each graph structure using the steady state random walk and compute a probability distribution of the walk visiting the vertices. Furthermore, we propose a new information theoretic criterion to measure the joint relevance of different pairwise feature combinations with respect to the target feature, through the Jensen-Shannon divergence measure between the probability distributions from the random walk on different graphs. By solving a quadratic programming problem, we use the new measure to automatically locate the subset of the most informative features, that have both low redundancy and strong discriminating power. Unlike most existing state-of-the-art feature selection methods, the proposed information theoretic feature selection method can accommodate both continuous and discrete target features. Experiments on the problem of P2P lending platforms in China demonstrate the effectiveness of the proposed method.