Clustering-based Discriminant Analysis Research Articles

Chronic pain patients can be classified into four groups: Clustering-based discriminant analysis of psychometric data from 4665 patients referred to a multidisciplinary pain centre (a SQRP study).

ObjectiveTo subgroup chronic pain patients using psychometric data and regress the variables most responsible for subgroup discrimination.DesignCross-sectional, registry-based study.Setting and subjectsChronic pain patients assessed at a multidisciplinary pain centre between 2008 and 2015.MethodsData from the Swedish quality registry for pain rehabilitation (SQRP) were retrieved and analysed by principal component analysis, hierarchical clustering analysis, and partial least squares–discriminant analysis.ResultsFour subgroups were identified. Group 1 was characterized by low “psychological strain”, the best relative situation concerning pain characteristics (intensity and spreading), the lowest frequency of fibromyalgia, as well as by a slightly older age. Group 2 was characterized by high “psychological strain” and by the most negative situation with respect to pain characteristics (intensity and spreading). Group 3 was characterized by high “social distress”, the longest pain durations, and a statistically higher frequency of females. The frequency of three neuropathic pain conditions was generally lower in this group. Group 4 was characterized by high psychological strain, low “social distress”, and high pain intensity.ConclusionsThe identification of these four clusters of chronic pain patients could be useful for the development of personalized rehabilitation programs. For example, the identification of a subgroup characterized mainly by high perceived “social distress” raises the question of how to best design interventions for such patients. Differentiating between clinically important subgroups and comparing how these subgroups respond to interventions is arguably an important area for further research.

Subclass Graph Embedding and a Marginal Fisher Analysis paradigm

Subspace learning techniques have been extensively used for dimensionality reduction (DR) in many pattern classification problem domains. Recently, methods like Subclass Discriminant Analysis (SDA) and Clustering-based Discriminant Analysis (CDA), which use subclass information for the discrimination between the data classes, have attracted much attention. In parallel, important work has been accomplished on Graph Embedding (GE), which is a general framework unifying several subspace learning techniques. In this paper, GE has been extended in order to integrate subclass discriminant information resulting to the novel Subclass Graph Embedding (SGE) framework, which is the main contribution of our work. It is proven that SGE encapsulates a diversity of both supervised and unsupervised unimodal methods like Locality Preserving Projections (LPP), Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA). The theoretical link of SDA and CDA methods with SGE is also established. Along these lines, it is shown that SGE comprises a generalization of the typical GE framework including subclass DR methods. Moreover, it allows for an easy utilization of kernels for confronting non-linearly separable data. Employing SGE, in this paper a novel DR algorithm, which uses subclass discriminant information, called Subclass Marginal Fisher Analysis (SMFA) has been proposed. Through a series of experiments on various real-world datasets, it is shown that SMFA outperforms in most of the cases the state-of-the-art demonstrating the efficacy and power of SGE as a platform to develop new methods.

Clustering-Based Discriminant Analysis for Eye Detection

This paper proposes three clustering-based discriminant analysis (CDA) models to address the problem that the Fisher linear discriminant may not be able to extract adequate features for satisfactory performance, especially for two class problems. The first CDA model, CDA-1, divides each class into a number of clusters by means of the k-means clustering technique. In this way, a new within-cluster scatter matrix Sw(c) and a new between-cluster scatter matrix Sb(c) are defined. The second and the third CDA models, CDA-2 and CDA-3, define a nonparametric form of the between-cluster scatter matrices N-Sb(c). The nonparametric nature of the between-cluster scatter matrices inherently leads to the derived features that preserve the structure important for classification. The difference between CDA-2 and CDA-3 is that the former computes the between-cluster matrix N-Sb(c) on a local basis, whereas the latter computes the between-cluster matrix N-Sb(c) on a global basis. This paper then presents an accurate CDA-based eye detection method. Experiments on three widely used face databases show the feasibility of the proposed three CDA models and the improved eye detection performance over some state-of-the-art methods.

Kernel clustering-based discriminant analysis

In this paper, a kernelized version of clustering-based discriminant analysis is proposed that we name KCDA. The main idea is to first map the original data into another high-dimensional space, and then to perform clustering-based discriminant analysis in the feature space. Kernel fuzzy c-means algorithm is used to do clustering for each class. A group of tests on two UCI standard benchmarks have been carried out that prove our proposed method is very promising.

2D clustering based discriminant analysis for 3D head model classification

This paper introduces a novel framework for 3D head model recognition based on the recently proposed 2D subspace analysis method. Two main contributions have been made. First, a 2D version of clustering-based discriminant analysis (CDA) is proposed, which combines the capability to model the multiple cluster structure embedded within a single class with the computational advantage that is characteristic of 2D subspace analysis methods. Second, we extend the applications of 2D subspace methods to the field of 3D head model classification by characterizing these models with 2D feature sets.