Bayesian principal component analysis (BPCA), a probabilistic reformulation of PCA with Bayesian model selection, is a systematic approach to determining the number of essential p...
Low-dimensional topic models have been proven very useful for modeling a large corpus of documents that share a relatively small number of topics. Dimensionality reduction tools s...
Sorting of the extracellularly recorded spikes is a basic prerequisite for analysis of the cooperative neural behavior and neural code. Fundamentally the sorting performance is deï...
Alexey N. Pavlov, Valeri A. Makarov, Ioulia Makaro...
An important problem in principal component analysis (PCA) is the estimation of the correct number of components to retain. PCA is most often used to reduce a set of observed vari...
In this paper, we study the problem of recovering a low-rank matrix (the principal components) from a highdimensional data matrix despite both small entry-wise noise and gross spar...
Zihan Zhou, Xiaodong Li, John Wright, Emmanuel J. ...
Background: Principal component analysis (PCA) has gained popularity as a method for the analysis of highdimensional genomic data. However, it is often difficult to interpret the ...
This paper discusses techniques for visualizing structure in video data and other data sets that represent time snapshots of physical phenomena. Individual frames of a movie are t...
The problem of optimization of subband coders for given input statistics has received considerable attention in recent literature. The goal in these works has been to maximize the...
Fuzzy c-varieties (FCV) is one of the clustering algorithms in which the prototypes are multi-dimensional linear varieties. The linear varieties are represented by some local prin...
We took a collection of 100 drum beats from popular music tracks and estimated the measure length and downbeat position of each one. Using these values, we normalized each pattern...