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The framework produces the DICE score, an indicator of the likely success of a project based on various measures. [2] DICE was originally developed by Perry Keenan, Kathleen Conlon, and Alan Jackson, all current or former partners at the Boston Consulting Group. [3] It was first published in the Harvard Business Review [4] in 2005.
Principal component analysis (PCA) is a linear dimensionality reduction technique with applications in exploratory data analysis, visualization and data preprocessing. The data is linearly transformed onto a new coordinate system such that the directions (principal components) capturing the largest variation in the data can be easily identified.
The 2014 guaranteed algorithm for the robust PCA problem (with the input matrix being = +) is an alternating minimization type algorithm. [12] The computational complexity is () where the input is the superposition of a low-rank (of rank ) and a sparse matrix of dimension and is the desired accuracy of the recovered solution, i.e., ‖ ^ ‖ where is the true low-rank component and ^ is the ...
Harvard Business Review 57.2 (1979): 81. Rockart, John F. "The changing role of the information systems executive: a critical success factors perspective." Sloan Management Review Fall 1982; 24, pp. 3–13; Rockart, John F., and Lauren S. Flannery. "The management of end user computing." Communications of the ACM 26.10 (1983): 776-784.
Some issues of Harvard Business Review. Harvard Business Review (HBR) [3] [4] is a general management magazine [5] [6] published by Harvard Business Publishing, a not-for-profit, independent corporation that is an affiliate of Harvard Business School. HBR is published six times a year [3] and is headquartered in Brighton, Massachusetts.
Functional principal component analysis (FPCA) is a statistical method for investigating the dominant modes of variation of functional data. Using this method, a random function is represented in the eigenbasis, which is an orthonormal basis of the Hilbert space L 2 that consists of the eigenfunctions of the autocovariance operator .