Normalized Cuts and Image Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Unsupervised Feature Selection Using Feature Similarity
IEEE Transactions on Pattern Analysis and Machine Intelligence
Suppressed fuzzy c-means clustering algorithm
Pattern Recognition Letters
Laplacian Eigenmaps for dimensionality reduction and data representation
Neural Computation
Simultaneous Feature Selection and Clustering Using Mixture Models
IEEE Transactions on Pattern Analysis and Machine Intelligence
Clustering through ranking on manifolds
ICML '05 Proceedings of the 22nd international conference on Machine learning
Discriminative cluster analysis
ICML '06 Proceedings of the 23rd international conference on Machine learning
Rapid and Brief Communication: Fuzzy discriminant analysis with kernel methods
Pattern Recognition
Computational and Theoretical Analysis of Null Space and Orthogonal Linear Discriminant Analysis
The Journal of Machine Learning Research
Adaptive dimension reduction using discriminant analysis and K-means clustering
Proceedings of the 24th international conference on Machine learning
Letters: Feature extraction using fuzzy inverse FDA
Neurocomputing
A complete fuzzy discriminant analysis approach for face recognition
Applied Soft Computing
A novel fuzzy clustering algorithm based on a fuzzy scatter matrix with optimality tests
Pattern Recognition Letters
Simultaneous model-based clustering and visualization in the Fisher discriminative subspace
Statistics and Computing
Optimality test for generalized FCM and its application to parameter selection
IEEE Transactions on Fuzzy Systems
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Linear Discriminant Analysis (LDA) is a classical statistical approach for supervised feature extraction and dimensionality reduction, hard c-means (HCM) is a classical unsupervised learning algorithm for clustering. Based on the analysis of the relationship between LDA and HCM, Linear Discriminant Analysis-guided adaptive subspace hard c-means clustering algorithm (LDA-HCM) had been proposed. LDA-HCM combines LDA and HCM into a coherent framework and can adaptively reduce the dimension of data while performing data clustering simultaneously. Seeing that LDA-HCM is still a hard clustering algorithm, we consider the fuzzy extension version of LDA-HCM in this paper. To this end, firstly, we propose a new optimization criterion of Fuzzy Linear Discriminant Analysis (FLDA) by extending the value of membership function in classical LDA from binary 0 or 1 into closed interval [0, 1]. In the meantime, we present an efficient algorithm for the proposed FLDA. Secondly, we show the close relationship between FLDA and Maximum Entropy Fuzzy Clustering Algorithm (MEFCA): they both are maximizing fuzzy between-class scatter and minimizing within-class scatter simultaneously. Finally, based on the above analysis, combining FLDA and MEFCA into a joint framework, we propose fuzzy Linear Discriminant Analysis-guided maximum entropy fuzzy clustering algorithm (FLDA-MEFCA). LDA-MEFCA is a natural and effective fuzzy extension of LDA-HCM. Due to the introduction of soft decision strategy, FLDA-MEFCA can yield fuzzy partition of data set and is more flexible than LDA-HCM. We also give the convergence proof of FLDA-MEFCA. Extensive experiments on a collection of benchmark data sets are presented to show the effectiveness of the proposed algorithm.