Consensus embedding: theory, algorithms and application to segmentation and classification of biomedical data.
Consensus embedding: theory, algorithms and application to segmentation and classification of biomedical data.
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共识嵌入：理论，算法以及对生物医学数据分割和分类的应用。
DOI：
10.1186/1471-2105-13-26
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发表时间：
2012-02-08
影响因子：
3
通讯作者：
Madabhushi A
中科院分区：
文献类型：
--
作者：
Viswanath S;Madabhushi A
降维 (DR) 可以从较高维的特征空间构建较低维的空间（嵌入），同时保留对象类的可区分性。然而，几种流行的灾难恢复方法对参数选择和/或数据中是否存在噪声很敏感。在本文中，我们提出了一种称为共识嵌入的新型 DR 技术，旨在通过生成和组合多个低维嵌入来克服这些问题，从而以类似于 Bagging 等集成分类器方案的方式利用它们之间的方差。我们演示了共识嵌入的理论特性，这表明它将产生一个稳定的嵌入解决方案，与任何单独的嵌入（通过主成分分析、图嵌入或局部线性嵌入等 DR 方案生成）相比，该解决方案可以更准确地保留信息。利用智能子采样（通过均值漂移）和代码并行化来提供该方案的有效实现。共识嵌入的应用在分类和聚类的背景下显示，适用于：(1) 10 个不同的合成大脑 MRI 图像上的白质和灰质图像分区，这些图像被 18 种不同的噪声和偏置场不均匀性组合损坏；(2) 4 个高维基因表达数据集的分类；(3) 从 2 个不同的高分辨率前列腺 MRI 数据集获得的 16 个图像切片上进行癌症检测（像素级）。在超过 200 个有关生物医学数据分类和分割的不同实验中，发现共识嵌入在所有考虑的应用中始终优于线性和非线性 DR 方法。我们提出了一种称为共识嵌入的新颖框架，它在降维中利用集成分类理论，允许应用于广泛的高维生物医学数据分类和分割问题。我们的通用框架可以改进成像和非成像数据的表示和分类。该算法为当前困扰 DR 方法的问题提供了一种有前景的解决方案，并可能扩展到生物医学数据分析的其他领域。
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. Applications of consensus embedding are shown in the context of classification and clustering as applied to: (1) image partitioning of white matter and gray matter on 10 different synthetic brain MRI images corrupted with 18 different combinations of noise and bias field inhomogeneity, (2) classification of 4 high-dimensional gene-expression datasets, (3) cancer detection (at a pixel-level) on 16 image slices obtained from 2 different high-resolution prostate MRI datasets. In over 200 different experiments concerning classification and segmentation of biomedical data, consensus embedding was found to consistently outperform both linear and non-linear DR methods within all applications considered. We have presented a novel framework termed consensus embedding which leverages ensemble classification theory within dimensionality reduction, allowing for application to a wide range of high-dimensional biomedical data classification and segmentation problems. Our generalizable framework allows for improved representation and classification in the context of both imaging and non-imaging data. The algorithm offers a promising solution to problems that currently plague DR methods, and may allow for extension to other areas of biomedical data analysis.
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DOI：
10.1109/tpami.2005.113
发表时间：
2005-06-01
影响因子：
23.6
作者：
Fred, ALN;Jain, AK
通讯作者：
Jain, AK
DOI：
10.1109/34.709601
发表时间：
1998-08-01
影响因子：
23.6
作者：
Ho, TK
通讯作者：
Ho, TK
DOI：
10.1109/34.1000236
发表时间：
2002-05-01
影响因子：
23.6
作者：
Comaniciu, D;Meer, P
通讯作者：
Meer, P
影响因子：
10.6
作者：
Carballido-Gamio, J;Belongie, SJ;Majumdar, S
通讯作者：
Majumdar, S
DOI：
10.1109/tsmc.1973.4309314
发表时间：
1973-01-01
期刊：
IEEE TRANSACTIONS ON SYSTEMS MAN AND CYBERNETICS
影响因子：
--
作者：
HARALICK, RM;SHANMUGAM, K;DINSTEIN, I
通讯作者：
DINSTEIN, I