Many areas of science depend on exploratory data analysis and visualization. The need to analyze large amounts of multivariate data raises the fundamental problem of dimensionality reduction: how to… (More)

Camera shake during exposure leads to objectionable image blur and ruins many photographs. Conventional blind deconvolution methods typically assume frequency-domain constraints on images, or overly… (More)

In this paper we propose a novel method for learning a Mahalan obis distance measure to be used in the KNN classification algorit hm. The algorithm directly maximizes a stochastic variant of the le… (More)

We describe a probabilistic approach to the task of placing objects, described by high-dimensional vectors or by pairwise dissimilarities, in a low-dimensional space in a way that preserves neighbor… (More)

I present an expectation-maximization (EM) algorithm for principal component analysis (PCA). The algorithm allows a few eigenvectors and eigenvalues to be extracted from large collections of high… (More)

We present an algorithm for learning a quadratic Gaussian me tric (Mahalanobis distance) for use in classification tasks. Our metho d relies on the simple geometric intuition that a good metric is… (More)

Factor analysis, principal component analysis, mixtures of gaussian clusters, vector quantization, Kalman filter models, and hidden Markov models can all be unified as variations of unsupervised… (More)

We propose a non-linear generative model for human motion data that uses an undirected model with binary latent variables and real-valued “visible” variables that represent joint angles. The latent… (More)

This paper proposes the combination of several ideas, some old and some new, from machine learning and speech processing. We review the max approximation to log spectrograms of mixtures, show why… (More)