Indexing of large scale patient set
First Claim
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1. A method for indexing data, comprising:
- formulating an objective function to index a dataset, a portion of the dataset including supervision information;
determining a data property component of the objective function which utilizes a property of the dataset to group data of the dataset, the determining the data property component including maximizing a variance of the dataset by applying a principal component analysis (PCA) and maximum variance unfolding (MVU), the (MVU) comprising forming an inner product of a data matrix and a learned partition hyperplane and maximizing overall pairwise distances in a projected space to reduce computational costs during the indexing, and the PCA comprising determining an eigenvector from a data covariance matrix with a largest corresponding eigenvector;
determining a supervised component of the objective function which utilizes the supervision information to group data of the dataset; and
optimizing the objective function using a processor based upon the data property component and the supervised component to partition a node into a plurality of child nodes.
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Abstract
Systems and methods for indexing data include formulating an objective function to index a dataset, a portion of the dataset including supervision information. A data property component of the objective function is determined, which utilizes a property of the dataset to group data of the dataset. A supervised component of the objective function is determined, which utilizes the supervision information to group data of the dataset. The objective function is optimized using a processor based upon the data property component and the supervised component to partition a node into a plurality of child nodes.
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12 Claims
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1. A method for indexing data, comprising:
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formulating an objective function to index a dataset, a portion of the dataset including supervision information; determining a data property component of the objective function which utilizes a property of the dataset to group data of the dataset, the determining the data property component including maximizing a variance of the dataset by applying a principal component analysis (PCA) and maximum variance unfolding (MVU), the (MVU) comprising forming an inner product of a data matrix and a learned partition hyperplane and maximizing overall pairwise distances in a projected space to reduce computational costs during the indexing, and the PCA comprising determining an eigenvector from a data covariance matrix with a largest corresponding eigenvector; determining a supervised component of the objective function which utilizes the supervision information to group data of the dataset; and optimizing the objective function using a processor based upon the data property component and the supervised component to partition a node into a plurality of child nodes. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12)
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