Title |
Consensus embedding: theory, algorithms and application to segmentation and classification of biomedical data
|
---|---|
Published in |
BMC Bioinformatics, February 2012
|
DOI | 10.1186/1471-2105-13-26 |
Pubmed ID | |
Authors |
Satish Viswanath, Anant Madabhushi |
Abstract |
Dimensionality reduction (DR) enables the construction of a lower dimensional space (embedding) from a higher dimensional feature space while preserving object-class discriminability. However several popular DR approaches suffer from sensitivity to choice of parameters and/or presence of noise in the data. In this paper, we present a novel DR technique known as consensus embedding that aims to overcome these problems by generating and combining multiple low-dimensional embeddings, hence exploiting the variance among them in a manner similar to ensemble classifier schemes such as Bagging. We demonstrate theoretical properties of consensus embedding which show that it will result in a single stable embedding solution that preserves information more accurately as compared to any individual embedding (generated via DR schemes such as Principal Component Analysis, Graph Embedding, or Locally Linear Embedding). Intelligent sub-sampling (via mean-shift) and code parallelization are utilized to provide for an efficient implementation of the scheme. |
X Demographics
Geographical breakdown
Country | Count | As % |
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United Kingdom | 2 | 67% |
United States | 1 | 33% |
Demographic breakdown
Type | Count | As % |
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Scientists | 1 | 33% |
Members of the public | 1 | 33% |
Science communicators (journalists, bloggers, editors) | 1 | 33% |
Mendeley readers
Geographical breakdown
Country | Count | As % |
---|---|---|
United Kingdom | 3 | 5% |
France | 2 | 4% |
Russia | 1 | 2% |
Unknown | 50 | 89% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Researcher | 19 | 34% |
Student > Ph. D. Student | 11 | 20% |
Student > Master | 6 | 11% |
Other | 5 | 9% |
Student > Bachelor | 3 | 5% |
Other | 6 | 11% |
Unknown | 6 | 11% |
Readers by discipline | Count | As % |
---|---|---|
Computer Science | 14 | 25% |
Medicine and Dentistry | 10 | 18% |
Agricultural and Biological Sciences | 10 | 18% |
Engineering | 5 | 9% |
Biochemistry, Genetics and Molecular Biology | 3 | 5% |
Other | 6 | 11% |
Unknown | 8 | 14% |