Separation of complex valued signals is a frequently arising problem in signal processing. For example, separation of convolutively mixed source signals involves computations on complex valued signals. In this article, it is assumed that the original, complex valued source signals are mutually statistically independent, and the problem is solved by the… (More)
Random projections have recently emerged as a powerful method for dimensionality reduction. Theoretical results indicate that the method preserves distances quite nicely; however, empirical results are sparse. We present experimental results on using random projection as a dimensionality reduction tool in a number of cases, where the high dimensionality of… (More)
The problem of analysing dynamically evolving textual data has arisen within the last few years. An example of such data is the discussion appearing in Internet chat lines. In this Letter a recently introduced source separation method, termed as complexity pursuit, is applied to the problem of finding topics in dynamical text and is compared against several… (More)
We present a novel probabilistic multiple cause model for binary observations. In contrast to other approaches, the model is linear and it infers reasons behind both observed and unobserved attributes with the aid of an explanatory variable. We exploit this distinctive feature of the method to automatically distinguish between attributes that are 'off' by… (More)
In this study we show experimental results on using Independent Component Analysis (ICA) and the Self-Organizing Map (SOM) in document analysis. Our documents are segments of spoken dialogues carried out over the telephone in a customer service, transcribed into text. The task is to analyze the topics of the discussions, and to group the discussions into… (More)
Presence–absence (0–1) observations are special in that often the absence of evidence is not evidence of absence. Here we develop an independent factor model, which has the unique capability to isolate the former as an independent discrete binary noise factor. This representation then forms the basis of inferring missed presences by means of denoising. This… (More)
We address the problem of interactive feature construction and denoising of binary data. To this end, we develop a variational ICA method, employing a multivariate Bernoulli likelihood and independent Beta source densities. We relate this to other binary data models, demonstrating its advantages in two application domains.
The data model of independent component analysis (ICA) gives a multivariate probability density that describes many kinds of sensory data better than classical models like Gaussian densities or Gaussian mixtures. When only a subset of the random variables is observed, ICA can be used for regression, i.e. to predict the missing observations. In this paper,… (More)