Principal component analysis (PCA) for binary data, known as logistic PCA, has become a
popular alternative to dimensionality reduction of binary data. It is motivated as an extension …

We investigate a generalized linear model for dimensionality reduction of binary data. The
model is related to principal component analysis (PCA) in the same way that logistic …

We develop a new principal components analysis (PCA) type dimension reduction method
for binary data. Different from the standard PCA which is defined on the observed data, the …

Principal component analysis (PCA) is very useful for a wide variety of data analysis tasks,
but its implicit connection to the Gaussian distribution can be undesirable for discrete data …

Abstract Principal Components Analysis (PCA) has become established as one of the key
tools for dimensionality reduction when dealing with real valued data. Approaches such as …

As the data size increases and the data structure becomes increasingly complex,
considering the nonlinearity, mixed measurement level data, simultaneous analysis and …

Principal component analysis (PCA) is one of the widely used matrix factorization
techniques for dimensionality reduction and revealing hidden factors that underlie sets of …

Principal components analysis (PCA) is a classic method for the reduction of dimensionality
of data in the form of n observations (or cases) of a vector with p variables. Contemporary …

Principal component analysis (PCA) is a commonly applied technique for dimensionality
reduction. PCA implicitly minimizes a squared loss function, which may be inappropriate for …

Principal component analysis (PCA) is a fundamental dimension reduction tool in statistics
and machine learning. For large and high-dimensional data, computing the PCA (ie, the …