Unsupervised feature selection using feature similarity

Mitra, Pabitra; Murthy, C. A.; Pal, Sankar K.

doi:10.1109/34.990133

Cited by 1,280 publications

(614 citation statements)

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“…The value of λ is zero when the features are linearly dependent and increases as the amount of dependence decreases. It has been proved that this index outperforms some conventional approaches to feature selection, such as branch and bound, sequential forward search, sequential floating forward search, and stepwise clustering, both in accuracy and CPU time [11]. See [11] for a detailed description of the S-Index.…”

Section: Dimension Reduction For the Spatial Featuresmentioning

confidence: 99%

“…This method does not need any searching and therefore is fast. A new feature similarity measure, called maximum information compression index (MICI), is introduced in [11]. Let Σ be the covariance matrix of random variables x and y.…”

Section: Dimension Reduction For the Spatial Featuresmentioning

confidence: 99%

“…But in this letter, all the spectral bands are used to extract the spatial information, after which an unsupervised feature selection approach using the feature similarity index (S-Index) [11], which is a fast and effective algorithm, is employed to select the optimal subset. S-Index is based on measuring similarity between features whereby redundancy therein is removed.…”

Section: Dimension Reduction For the Spatial Featuresmentioning

confidence: 99%

See 2 more Smart Citations

Classification and Extraction of Spatial Features in Urban Areas Using High-Resolution Multispectral Imagery

Huang

Zhang

2007

IEEE Geosci. Remote Sensing Lett.

160

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Abstract-Classification and extraction of spatial features are investigated in urban areas from high spatial resolution multispectral imagery. The proposed approach consists of three steps. First, as an extension of our previous work [pixel shape index (PSI)], a structural feature set (SFS) is proposed to extract the statistical features of the direction-lines histogram. Second, some methods of dimension reduction, including independent component analysis, decision boundary feature extraction, and the similarity-index feature selection, are implemented for the proposed SFS to reduce information redundancy. Third, four classifiers, the maximum-likelihood classifier, backpropagation neural network, probability neural network based on expectationmaximization training, and support vector machine, are compared to assess SFS and other spatial feature sets. We evaluate the proposed approach on two QuickBird datasets, and the results show that the new set of reduced spatial features has better performance than the existing length-width extraction algorithm and PSI.Index Terms-Feature extraction, feature selection, high spatial resolution multispectral (HSRM) imagery, spatial feature set.

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Section: Dimension Reduction For the Spatial Featuresmentioning

confidence: 99%

Section: Dimension Reduction For the Spatial Featuresmentioning

confidence: 99%

Section: Dimension Reduction For the Spatial Featuresmentioning

confidence: 99%

See 1 more Smart Citation

Classification and Extraction of Spatial Features in Urban Areas Using High-Resolution Multispectral Imagery

Huang

Zhang

2007

IEEE Geosci. Remote Sensing Lett.

160

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“…Information score (IS): the authors in [27] present an information measure for unlabeled data, shown in Equation 2. The RBF similarity matrix S is used to calculate the entropy of the data, measuring its randomness.…”

Section: Feature Importance Measuresmentioning

confidence: 99%

Filter Feature Selection for One-Class Classification

Lorena

Carvalho

Lorena

2014

J Intell Robot Syst

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In one-class classification problems all training examples belong to a single class. The absence of counter-examples represents a challenge to traditional Machine Learning and pre-processing techniques. This is the case of various feature selection techniques for labeled data. The selection of the most relevant features from a dataset usually benefits the performance obtained by classification algorithms. Despite the relevance of this issue, few techniques have been proposed for feature selection in one-class classification problems. Moreover, most of the existent techniques are wrapper approaches, which have to rely on a specific classification algorithm for feature selection, or aggregation techniques. This paper proposes a new filter feature selection approach for one-class classification. First, five feature selection measures from different paradigms are here employed or adapted to the one-class scenario. Next, the feature rankings produced by these measures are combined using different aggregation strategies. The proposed approach was able to reduce the size of the feature sets while maintaining or even improving the predictive performance obtained by the one-class classifier.

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“…Many tracking algorithms have been proposed and implemented in applications to overcome these disturbing problems. These algorithms can be divided into three main categories: the featurebased tracking algorithms (Mitra et al, 2002;Tissainayagam and Suter, 2005;Nickels and Hutchinson, 2002), the contour-based tracking algorithms (Paragios and Deriche, 2000;Freedman and Zhang, 2004;Linlin et al, 2009), and the region-based tracking algorithms (Bascle and Deriche, 1995;Jepson et al, 2003). In the last category, the region content is either used directly with template tracking or represented by a nonparametric description, such as a histogram.…”

Section: Introductionmentioning

confidence: 99%