PURPOSE Conventional instruments are known to result in high numbers of outliers in restoring femoral component rotation primarily due to fixed degree of external rotation resection relative to the posterior condylar line (PCL). Outliers can be reduced by determining the patient specific posterior condylar angle (PCA) preoperatively or intraoperatively. There is a paucity of methods that can be...

Principal component analyses (PCA) has been widely used in reduction of the dimensionality of datasets, classification, feature extraction, etc. It has been combined with many other algorithms such as EM (expectation-maximization), ANN (artificial neural network), probabilistic models, statistic analyses, etc., and has its own development such as MPCA (moving PCA), MS-PCA (multi-scale PCA), etc...

In this paper, we have proposed a novel method for two-dimensional shape object recognition and retrieval. The proposed method is based on Ridgelet Principal Component Analysis (Ridgelet PCA). In our proposed approach we first use the ridgelet transform to extract line singularity features and point singularity features by applying the radon and wavelet transform respectively and then applying ...

Principal component analysis (PCA) is a widely used signal processing technique. Instead of performing PCA in the data space, we consider the problem of sparse PCA in a potentially higher dimensional latent space. To do so, we zero-out groups of variables using vector `0 regularization. The estimation is based on the maximization of the penalized log-likelihood, for which we develop an efficien...

Principal Component Analysis (PCA) as a tool for dimensionality reduction is widely used in many areas. In the area of bioinformatics, each involved variable corresponds to a specific gene. In order to improve the robustness of PCA-based method, this paper proposes a novel graph-Laplacian PCA algorithm by adopting L1/2 constraint (L1/2 gLPCA) on error function for feature (gene) extraction. The...

Extending the classical principal component analysis (PCA), the kernel PCA (Schölkopf, Smola and Müller, 1998) effectively extracts nonlinear structures of high dimensional data. But similar to PCA, the kernel PCA can be sensitive to outliers. Various approaches have been proposed in the literature to robustify the classical PCA. However, it is not immediately clear how these approaches can be ...

The variational Bayesian nonlinear blind source separation method introduced by Lappalainen and Honkela in 2000 is initialised with linear principal component analysis (PCA). Because of the multilayer perceptron (MLP) network used to model the nonlinearity, the method is susceptible to local minima and therefore sensitive to the initialisation used. As the method is used for nonlinear separatio...

Estimating intrinsic dimensionality of data is a classic problem in pattern recognition and statistics. Principal Component Analysis (PCA) is a powerful tool in discovering dimensionality of data sets with a linear structure; it, however, becomes ineffective when data have a nonlinear structure. In this paper, we propose a new PCA-based method to estimate intrinsic dimension of data with nonlin...