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Principal component analysis (PCA) is one of the most crucial dimensionality reduction methods and widely used in satellite image analysis, face recognition, social network feature extraction and other application scenarios. But it is fragile because of its quadratic error criterion when faced with outliers. There are many robust PCAs to solve this problem, however, when extended to the high-dimensional...
Gait analysis of human plays a significant role in maintaining the well-being of our mobility and healthcare, and it can be used for various e-healthcare systems for fast medical prognosis and diagnosis. In this paper we have developed a novel body sensor network based recognition system to identify the specific gait pattern of Parkinson's disease (PD). Firstly, a BSN with 16 nodes is used to acquire...
The new locally preserving projections algorithm is proposed in this paper which is based on Bayesian criteria and adapted improved iterative self-organize data analysis. The experiment shows that the new algorithm can put forward the optimum number of dimensions and be more available than principle component analysis. That is because it takes into account the relation the number of between dimensions...
Assuming that high-dimensional data are generated from intrinsic variables with lower dimensions, several key manifold-learning algorithms can help effectively analyze and visualize such data.
The aim of this paper is to learn a linear principal component using the nature of support vector machines (SVMs). To this end, a complete SVM-like framework of linear PCA (SVPCA) for deciding the projection direction is constructed, where new expected risk and margin are introduced. Within this framework, a new semi-definite programming problem for maximizing the margin is formulated and a new definition...
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