Helsinki Institute for Information Technology

Self-Organization and Associative Memory

Self-Organization and Associative Memory

A Fast Fixed-Point Algorithm for Independent Component Analysis

We introduce a novel fast algorithm for independent component analysis, which can be used for blind source separation and feature extraction. We show how a neural network learni...

LoRDEC: accurate and efficient long read error correction

Abstract Motivation: PacBio single molecule real-time sequencing is a third-generation sequencing technique producing long reads, with comparatively lower throughput and higher ...

A class of neural networks for independent component analysis

Independent component analysis (ICA) is a recently developed, useful extension of standard principal component analysis (PCA). The ICA model is utilized mainly in blind separati...

Dense genomic sampling identifies highways of pneumococcal recombination

Statistical pattern recognition with neural networks: benchmarking studies

Three basic types of neural-like networks (backpropagation network, Boltzmann machine, and learning vector quantization), were applied to two representative artificial statistic...

Variants of self-organizing maps

Self-organizing maps have a bearing on traditional vector quantization. A characteristic that makes them more closely resemble certain biological brain maps, however, is the spa...

Independent component analysis by general nonlinear Hebbian-like learning rules

Gaussian moments for noisy independent component analysis

A novel approach for the problem of estimating the data model of independent component analysis (or blind source separation) in the presence of Gaussian noise is introduced. We ...