Advances in Independent Component Analysis and Learning by Ella Bingham, Samuel Kaski, Jorma Laaksonen, Jouko Lampinen

By Ella Bingham, Samuel Kaski, Jorma Laaksonen, Jouko Lampinen

In honour of Professor Erkki Oja, one of many pioneers of self sustaining part research (ICA), this booklet experiences key advances within the concept and alertness of ICA, in addition to its impact on sign processing, trend acceptance, laptop studying, and knowledge mining.

Examples of subject matters that have built from the advances of ICA, that are coated within the publication are:

  • A unifying probabilistic version for PCA and ICA
  • Optimization equipment for matrix decompositions
  • Insights into the FastICA algorithm
  • Unsupervised deep studying
  • Machine imaginative and prescient and picture retrieval
  • A evaluate of advancements within the thought and purposes of self reliant part research, and its impact in vital components akin to statistical sign processing, development attractiveness and deep learning.
  • A assorted set of software fields, starting from laptop imaginative and prescient to technological know-how coverage data.
  • Contributions from major researchers within the field.

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Additional resources for Advances in Independent Component Analysis and Learning Machines

Example text

A uniform distribution throughout the m-dimensional unit hypercube centered at [0 · · · 0], when projected onto the m-dimensional unit hypersphere, tends to concentrate probability in the m−1/2 [±1 ± 1 · · · ± 1]T directions of m-dimensional space, an undesirable situation from the prospective of FastICA convergence. Moreover, the situation is further made more challenging if all of the sources have the same kurtosis, in which case convergence has been shown to be slower than in the unequal-kurtosis case.

Discussion. This theorem takes that for reasonable distribution assumptions on the initial ICI, the ICI at time t is bounded by a function consisting of the product of a linear-converging term and a cubically converging term. Cubic convergence is the described behavior of two-source FastICA in a deterministic setting, and it is ultimately attained under stochastic initial conditions of the separation system vector if the initial distribution of the ICI is bounded away from unity, although it may take a number of iterations before this cubically converging term dominates the expression.

The results in this subsection are based on the following simple but very nice observation: In the convergence analysis of FastICA, ordering of the coefficients within the update relations does not matter. Hence, the coefficients within ct can be reordered to obtain a structured distribution of the coefficients. d. elements and scaling is ignored. Despite these facts, the integrals become much simpler in at least one specific case. Suppose the elements of the unnormalized vector c0 = [ˆc10 · · · cˆ m0 ]T are uniformly distributed on the interval [0, 1], such that p(ˆc10 , cˆ 20 , .

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