Topics
numerical optimizationCultivar (cultivated variety)
MarkovElection
principal component analysisAnatomical structure
algorithmMusic genre
dimension reductionElection
alanineChemical substance
We present a method for simultaneous dimension reduction and metastability analysis of high dimensional time series. The approach is based on the combination of hidden Markov models (HMMs) and principal component analysis. We derive optimal estimators for the loglikelihood functional and employ the Expectation Maximization algorithm for its numerical optimization. We demonstrate the performance of the method on a generic 102-dimensional example, apply the new HMM-PCA algorithm to a molecular dynamics simulation of 12–alanine in water and interpret the results
We study the performance of principal component analysis (PCA). In particular, we consider the probl...
We propose a new high dimensional semiparametric principal component analysis (PCA) method, named Co...
In this paper, we propose a general dimensionality reduction method for data generated from a very b...
Advances in data acquisition and emergence of new sources of data, in recent years, have led to gene...
Motivated from a changing market environment over time, we consider high-dimensional data such as fi...
Dimension reduction techniques are at the core of the statistical analysis of high-dimensional and f...
Motivated from a changing market environment over time, we consider high-dimensional data such as fi...
© 2019 Elsevier B.V. Dimension reduction is often an important step in the analysis of high-dimensio...
Dimension reduction techniques are at the core of the statistical analysis of high-dimensional and f...
We present a novel method for the identification of the most important metastable states of a system...
We propose a new high dimensional semiparametric principal component analysis (PCA) method, named Co...
We consider the dimensionality-reduction problem (finding a subspace approximation of observed data)...
We analyze an algorithm based on principal component analysis (PCA) for detecting the dimension k of...
Summary. Exponential principal component analysis (e-PCA) provides a frame-work for appropriately de...
In this paper, we consider a constrained principal component analysis (PCA) for the projection of hi...
We study the performance of principal component analysis (PCA). In particular, we consider the probl...
We propose a new high dimensional semiparametric principal component analysis (PCA) method, named Co...
In this paper, we propose a general dimensionality reduction method for data generated from a very b...
Advances in data acquisition and emergence of new sources of data, in recent years, have led to gene...
Motivated from a changing market environment over time, we consider high-dimensional data such as fi...
Dimension reduction techniques are at the core of the statistical analysis of high-dimensional and f...
Motivated from a changing market environment over time, we consider high-dimensional data such as fi...
© 2019 Elsevier B.V. Dimension reduction is often an important step in the analysis of high-dimensio...
Dimension reduction techniques are at the core of the statistical analysis of high-dimensional and f...
We present a novel method for the identification of the most important metastable states of a system...
We propose a new high dimensional semiparametric principal component analysis (PCA) method, named Co...
We consider the dimensionality-reduction problem (finding a subspace approximation of observed data)...
We analyze an algorithm based on principal component analysis (PCA) for detecting the dimension k of...
Summary. Exponential principal component analysis (e-PCA) provides a frame-work for appropriately de...
In this paper, we consider a constrained principal component analysis (PCA) for the projection of hi...
We study the performance of principal component analysis (PCA). In particular, we consider the probl...
We propose a new high dimensional semiparametric principal component analysis (PCA) method, named Co...
In this paper, we propose a general dimensionality reduction method for data generated from a very b...
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