On Similarity Preserving Feature Selection

Zhao, Zheng; Wang, Lei; Liu, Huan; Ye, Jieping

doi:10.1109/tkde.2011.222

Cited by 290 publications

(120 citation statements)

References 40 publications

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“…The last term is the diagonal vector of hyperedge weight W H and enjoys the sparse property, i.e., the weights of useless hyperedges will be set to 0. To be more speciÞc, the Þrst term aims to select k (k<d )features,basedon which best preserves the sample similarity as speciÞed by a predeÞned similarity matrix K.H e r e ,K is constructed using the Fisher Kernel in supervised learning [2] and by a Gaussian Kernel in unsupervised learning. However,…”

Section: Proposed Framework For Feature Selectionmentioning

confidence: 99%

“…• SPFS [2]: The basic idea of SPFS is to pursue a transformation matrix, which 360 transform the high-dimensional data to a low-dimensional data, to maximally preserve the global similarity structure of original data.…”

Section: Experiments Setupmentioning

confidence: 99%

“…Accordingtothewayinwhichlabel information is utilized, feature selection algorithms can be categorized as a) supervised algorithms, b) unsupervised algorithms or c) semi-supervised algorithms. Examples of supervised feature selection algorithms include the FisherScore(FScore) [1],similarity preserving feature selection (SPFS) [2], minimum redundancy maximum relevance 10 (mRMR) [3], local-learning based feature selection (LLFS) [4], robust feature selection via ℓ 2,1 -norm minimization (L21RFS) [5] and the Trace ratio [6], which only use labeled training data for feature selection. When sufÞcientl a b e l e dt r a i n i n gs a m p l e s are to used, supervised feature selection is a reliable alternative, which selects discriminative features by exploiting class labels.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Joint hypergraph learning and sparse regression for feature selection

Zhang

Bai

Liang

et al. 2017

Pattern Recognition

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In this paper, we propose a uniÞed framework for improved structure estimation and feature selection. Most existing graph-based feature selection methods utilise a static representation of the structure of the available data based on the Laplacian matrix of a simple graph. Here on the other hand, we perform data structure learning and feature selection simultaneously. To improve the estimation of the manifold representing the structure of the selected features, we use a higher order description of the neighbourhood structures present in the available data using hypergraph learning. This allows those features which participate in the most signiÞcant higher order relations to be selected, and the remainder discarded, through a sparsiÞcation process. We formulate a single objective function to capture and regularise the hypergraph weight estimation and feature selection processes. Finally, we present an optimization algorithm to recover the hyper graph weights and a sparse set of feature selection indicators. This process offers a number of advantages. First, by adjusting the hypergraph weights, we preserve high-order neighborhood relations reßected in theoriginaldata,whichcannot be modeled by a simple graph. Moreover, our objective function captures the global discriminative structure of the features in the data. Comprehensive experiments on 9 benchmark data sets show that our method achieves statistically signiÞcant improvement over state-of-art feature selection methods, supporting the effectiveness of the proposed method.

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Section: Proposed Framework For Feature Selectionmentioning

confidence: 99%

Section: Experiments Setupmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Joint hypergraph learning and sparse regression for feature selection

Zhang

Bai

Liang

et al. 2017

Pattern Recognition

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“…We also notice that a number of existing feature selection criteria implicitly select features that preserve sample relationship, which can be inferred from either a predefined distance metric or label information [25]. This indicates that it would be beneficial to incorporate sample relationship into the feature selection algorithm.…”

Section: Ig(x Y) = H(x) − H(x|y) = H(y) − H(y|x)mentioning

confidence: 99%

P2P Lending Analysis Using the Most Relevant Graph-Based Features

Cui

Bai

Wang

et al. 2016

Lecture Notes in Computer Science

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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.

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“…Unsupervised feature selection focus on a target concept rather than on class labels, where observations that are close to each other in the feature space should belong to the same target concept [41,42]. Some approaches that follow this principle using spectral graph theory are SPEC [41] and Laplacian Score [22].…”

Section: Sequential Forward Selection (Sfs) and Sequential Backward Ementioning

confidence: 99%

Profit-based feature selection using support vector machines – General framework and an application for customer retention

Maldonado

Flores

Verbraken

et al. 2015

Applied Soft Computing

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A c c e p t e d M a n u s c r i p t  A novel profit-based feature selection method for churn prediction with SVM is presented.  A backward elimination algorithm is performed to maximize the profit of a retention campaign. Our experiments on churn prediction datasets underline the potential of the proposed approaches. *Highlights (for review)Page 2 of 36 A c c e p t e d M a n u s c r i p Effective churners Outflow New customers InflowConsists of N customers with average customer lifetime value CLV.The cost of contacting a customer is f.The cost of an incentive offer is dWould-be churnersClassified as churners (η η η η)Would-be churnersClassified as nonchurners (1-η η η η)Customer Churn Management Campaign AbstractChurn prediction is an important application of classification models that identify those customers most likely to attrite based on their respective characteristics described by e.g. socio-demographic and behavioral variables.Since nowadays more and more of such features are captured and stored in the respective computational systems, an appropriate handling of the resulting information overload becomes a highly relevant issue when it comes to build customer retention systems based on churn prediction models. As a consequence, feature selection is an important step of the classifier construction process. Most feature selection techniques; however, are based on statistically inspired validation criteria, which not necessarily lead to models that optimize goals specified by the respective organization. In this paper we propose a profit-driven approach for classifier construction and simultaneous variable selection based on Support Vector Machines. Experimental results show that our models outperform conventional techniques for feature selection achieving superior performance with respect to business-related goals.

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On Similarity Preserving Feature Selection

Cited by 290 publications

References 40 publications

Joint hypergraph learning and sparse regression for feature selection

Joint hypergraph learning and sparse regression for feature selection

P2P Lending Analysis Using the Most Relevant Graph-Based Features

Profit-based feature selection using support vector machines – General framework and an application for customer retention

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