Unsupervised Feature Selection Using Feature Similarity

By Pabitra Mitra, C. A. Murthy, Sankar Kumar Pal. 2002

1,519 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition, Molecular Biology

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Citations in the corpus

A practical tutorial on autoencoders for nonlinear feature fusion: Taxonomy, models, software and guidelines (2018, background): “The former approach usually relies on correlation or mutual information between input and output variables [24, 25], while the latter tends to avoid redundancy among features [26].”
Feature Selection for Clustering: A Review (2018, background): “Feature selection has been successfully applied in many real applications, such as pattern recognition [28, 58, 46], text categorization [74, 31, 52], Image processing [28, 56], bioinformatics [1, 2], and so forth.”
Reconstruction-based Unsupervised Feature Selection: An Embedded Approach (2017, background): “) should also preserve feature structures in the original feature space since those structures play important roles in many real-world applications such as natural language processing and bioinformatics [Mitra et al., 2002; Tang et al., 2014].”
A survey on feature selection methods (2014, background): “Several papers that attempt to solve the feature selection using unsupervised learning can be found in [7,62–65].”
Subspace clustering for high dimensional data: a review (2004, background): “partition the original feature set into clusters based on a similarity function and select representatives from each group to form the sample [57].”
Feature Selection for High-Dimensional Data: A Fast Correlation-Based Filter Solution (2003, background): “Other measures in this category are basically variations of the above formula, such as least square regression error and maximal information compression index (Mitra et al., 2002).”
Hybrid attribute reduction based on a novel fuzzy-rough model and information granulation (2007, background): “The other is called filter, which selects attributes with a significance measure, independent of learning algorithms, such as distance [6], information gain [7], consistency [8], similarity [9,10] and dependency [11].”
Feature Selection Using Principal Component Analysis (2010, background): “In the past years many concepts and approaches such as the mutual information [1], feature similarity [2], loss-margin of nearest neighbor classification [3], ant colony optimization [4] and genetic algorithms [5] have been proposed for feature selection.”
A Graphbased Framework for Multi-Task Multi-View Learning (2011, methodology): “The ﬁlter models are independent of the clustering algorithm, such as the feature selection algorithm based on maximum information compression index [23], the feature selection method using distance-based entropy [9], and the feature selection method based on Laplacian score [14].”
Advances in Hyperspectral Image and Signal Processing: A Comprehensive Overview of the State of the Art (2017, neutral): “In the same manner, conventional unsupervised feature selection methods for DR select a subset of features from the original data according to a specific criterion, such as linear prediction error [28], entropy [29], or mutual information (by minimizing dependency) [30].”
Unsupervised feature selection for principal components analysis (2008, methodology): “[31, 13] propose heuristic approaches for unsupervised feature selection for clustering algorithms.”
Feature Selection: A literature Review (2014, neutral): “Dependency Bobrowski’s [49] CFS [27], RRESET, [56], POE+ACC [57], DVMM [58] Mitra’s [59],”
Clustering-Guided Sparse Structural Learning for Unsupervised Feature Selection (2014, methodology): “The filter algorithms [7], [20]–[22] use a proxy measure instead of the error rate to score a feature subset.”
Toward integrating feature selection algorithms for classification and clustering (2005, background): “Feature selection has been a fertile field of research and development since the 1970s in statistical pattern recognition [5], [40], [63], [81], [90], machine learning [6], [41], [43], [44], and data mining [17], [18], [42], and widely applied to many fields such as text categorization [50], [70],…”
Variable Selection for Model-Based Clustering (2006, methodology): “In a model-based clustering framework, work has been done on this problem by Law, Jain, and Figueiredo (2002), Vaithyanathan and Dom (1999), Liu, Zhang, Palumbo, and Lawrence (2003), Ding, He, Zha, and Simon (2002), Chakrabarti and Mehrotra (2000), Mitra, Murthy, and Pal (2002), and Talavera (2000).”