Handbook of Research on Machine Learning Applications and Trends

Gorban, A.N.; Zinovyev, Andreï

doi:10.4018/978-1-60566-766-9

Cited by 145 publications

(14 citation statements)

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“…Since Pearson [35] first invented and defined PCA through approximating multivariate distributions by lines and planes in 1901, researchers have defined PCA from different aspects [36], [37]. Among these definitions, using covariance matrix of the training samples to define PCA is very popular in pattern recognition and machine learning community.…”

Section: Review Of the Pcamentioning

confidence: 99%

Modified Principal Component Analysis: An Integration of Multiple Similarity Subspace Models

Fan

Xu²,

Zuo

et al. 2014

IEEE Trans. Neural Netw. Learning Syst.

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We modify the conventional principal component analysis (PCA) and propose a novel subspace learning framework, modified PCA (MPCA), using multiple similarity measurements. MPCA computes three similarity matrices exploiting the similarity measurements: 1) mutual information; 2) angle information; and 3) Gaussian kernel similarity. We employ the eigenvectors of similarity matrices to produce new subspaces, referred to as similarity subspaces. A new integrated similarity subspace is then generated using a novel feature selection approach. This approach needs to construct a kind of vector set, termed weak machine cell (WMC), which contains an appropriate number of the eigenvectors spanning the similarity subspaces. Combining the wrapper method and the forward selection scheme, MPCA selects a WMC at a time that has a powerful discriminative capability to classify samples. MPCA is very suitable for the application scenarios in which the number of the training samples is less than the data dimensionality. MPCA outperforms the other state-of-the-art PCA-based methods in terms of both classification accuracy and clustering result. In addition, MPCA can be applied to face image reconstruction. MPCA can use other types of similarity measurements. Extensive experiments on many popular real-world data sets, such as face databases, show that MPCA achieves desirable classification results, as well as has a powerful capability to represent data.

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Section: Review Of the Pcamentioning

confidence: 99%

Modified Principal Component Analysis: An Integration of Multiple Similarity Subspace Models

Fan

Xu²,

Zuo

et al. 2014

IEEE Trans. Neural Netw. Learning Syst.

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show abstract

“…Transfer learning is another area of important research activity. The goal of transfer learning is to improve learning in the target learning task by leveraging knowledge from an existing source task (9). Given challenges in obtaining sufficient data for target Patient Derived Xenografts (PDXs), where tumors are grown in mouse host animals, ongoing transfer learning work holds promise for learning on cell lines as a source for the target PDX model predictions.…”

Section: Ai and Large-scale Computing To Predict Tumor Treatment Respmentioning

confidence: 99%

AI Meets Exascale Computing: Advancing Cancer Research With Large-Scale High Performance Computing

et al. 2019

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The application of data science in cancer research has been boosted by major advances in three primary areas: (1) Data: diversity, amount, and availability of biomedical data; (2) Advances in Artificial Intelligence (AI) and Machine Learning (ML) algorithms that enable learning from complex, large-scale data; and (3) Advances in computer architectures allowing unprecedented acceleration of simulation and machine learning algorithms. These advances help build in silico ML models that can provide transformative insights from data including: molecular dynamics simulations, next-generation sequencing, omics, imaging, and unstructured clinical text documents. Unique challenges persist, however, in building ML models related to cancer, including: (1) access, sharing, labeling, and integration of multimodal and multi-institutional data across different cancer types; (2) developing AI models for cancer research capable of scaling on next generation high performance computers; and (3) assessing robustness and reliability in the AI models. In this paper, we review the National Cancer Institute (NCI) -Department of Energy (DOE) collaboration, Joint Design of Advanced Computing Solutions for Cancer (JDACS4C), a multi-institution collaborative effort focused on advancing computing and data technologies to accelerate cancer research on three levels: molecular, cellular, and population. This collaboration integrates various types of generated data, pre-exascale compute resources, and advances in ML models to increase understanding of basic cancer biology, identify promising new treatment options, predict outcomes, and eventually prescribe specialized treatments for patients with cancer.

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“…Using such a maximum likelihood estimate for σ, and assuming further that the dimension p of the Gibbs manifold is fixed from the outset, the remaining optimization of (the orientation of) G reduces to maximizing the weighted average of the relative entropies {S(πμ G (µ i ) μ)}. In the Gaussian regime, this is tantamount to the optimization task known in statistics as "principal component analysis" [33][34][35][36][37][38][39][40]. Now I turn to the general case in which there is an arbitrary given reference state, and where both the dimension and the orientation of the explanatory level of description are to be inferred.…”

Section: Assessing Thermalizationmentioning

confidence: 99%

“…In such a generic setting, the task is to infer the optimal dimension and orientation of the lower-dimensional latent space. Problems of this type can be tackled with a variety of statistical techniques such as factor analysis or principal component analysis [33][34][35][36][37][38][39][40]. In the present paper, I shall build on these generic techniques to develop a statistical framework tailored to the relevant task of assessing whether or not thermalization has occurred, and if so, inferring the most plausible set of constants of the motion.…”

Section: Introductionmentioning

confidence: 99%

Assessing thermalization and estimating the Hamiltonian with output data only

Rau

2011

Phys. Rev. A

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I consider the generic situation where a finite number of identical test systems in varying (possibly unknown) initial states are subjected independently to the same unknown process. I show how one can infer from the output data alone whether the process in question induces thermalization and, if so, which constants of the motion characterize the final equilibrium states. In case thermalization does occur and there is no evidence for constants of the motion other than energy, I further show how the same output data can be used to estimate the test systems' effective Hamiltonian. For both inference tasks I devise a statistical framework inspired by the generic techniques of factor and principal component analysis. I illustrate its use in the simple example of qubits.

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Handbook of Research on Machine Learning Applications and Trends

Cited by 145 publications

References 0 publications

Modified Principal Component Analysis: An Integration of Multiple Similarity Subspace Models

Modified Principal Component Analysis: An Integration of Multiple Similarity Subspace Models

AI Meets Exascale Computing: Advancing Cancer Research With Large-Scale High Performance Computing

Assessing thermalization and estimating the Hamiltonian with output data only

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