Head pose is an important indicator of a person’s focus of attention. Also, head pose estimation can be used as the front-end analysis for multi-view face analysis. For example, face recognition and identification algorithms are usually view dependent. Pose classification can help such face recognition systems to select the best view model. Subspace analysis has been widely used for head pose e...

The likelihood-informed subspace (LIS) method offers a viable route to reducing the dimensionality of high-dimensional probability distributions arising in Bayesian inference. LIS identifies an intrinsic low-dimensional linear where target distribution differs most from some tractable reference distribution. Such can be identified using leading eigenvectors Gram matrix gradient log-likelihood f...

Principal Subspace Analysis (PSA) -- and its sibling, Component (PCA) is one of the most popular approaches for dimensionality reduction in signal processing machine learning. But centralized PSA/PCA solutions are fast becoming irrelevant modern era big data, which number samples and/or often exceed storage computational capabilities individual machines. This has led to study distributed soluti...

We investigate a Gaussian mixture model (GMM) with component means constrained in a pre-selected subspace. Applications to classification and clustering are explored. An EM-type estimation algorithm is derived. We prove that the subspace containing the component means of a GMM with a common covariance matrix also contains the modes of the density and the class means. This motivates us to find a...

Analysis of high dimensional data is a research area since many years. Analysts can detect similarity of data points within a cluster. Subspace clustering detects useful dimensions in clustering high dimensional dataset. Visualization allows a better insight of subspace clusters. However, displaying such high dimensional database clusters on the 2-dimensional display is a challenging task. We p...

The power iteration is a classical method for computing the eigenvector associated with the largest eigenvalue of a matrix. The subspace iteration is an extension of the power iteration where the subspace spanned by n largest eigenvectors of a matrix, is determined. The natural power iteration is an exemplary instance of the subspace iteration, providing a general framework for many principal s...