In this task, you are given a part of an article. Your task is to generate headline (title) for this text. Preferred headlines are under fifteen words.

Motivated by the previously developed multilevel aggregation method for solving structural analysis problems a novel two-level aggregation approach for efficient iterative solution of Principal Component Analysis (PCA) problems is proposed. The course aggregation model of the original covariance matrix is used in the iterative solution of the eigenvalue problem by a power iterations method. The method is tested on several data sets consisting of large number of text documents.
ITERATIVE AGGREGATION METHOD FOR SOLVING PRINCIPAL COMPONENT ANALYSIS PROBLEMS

This paper presents a theoretical, idealized model of the thinking process with the following characteristics: 1) the model can produce complex thought sequences and can be generalized to new inputs, 2) it can receive and maintain input information indefinitely for the generation of thoughts and later use, and 3) it supports learning while executing. The crux of the model lies within the concept of internal consistency, or the generated thoughts should always be consistent with the inputs from which they are created. Its merit, apart from the capability to generate new creative thoughts from an internal mechanism, depends on the potential to help training to generalize better. This is consequently enabled by separating input information into several parts to be handled by different processing components with a focus mechanism to fetch information for each. This modularized view with the focus binds the model with the computationally capable Turing machines. And as a final remark, this paper constructively shows that the computational complexity of the model is at least, if not surpass, that of a universal Turing machine.
(Yet) Another Theoretical Model of Thinking

Minimum error rate training (MERT) is a widely used training procedure for statistical machine translation. A general problem of this approach is that the search space is easy to converge to a local optimum and the acquired weight set is not in accord with the real distribution of feature functions. This paper introduces coordinate system selection (RSS) into the search algorithm for MERT. Contrary to previous approaches in which every dimension only corresponds to one independent feature function, we create several coordinate systems by moving one of the dimensions to a new direction. The basic idea is quite simple but critical that the training procedure of MERT should be based on a coordinate system formed by search directions but not directly on feature functions. Experiments show that by selecting coordinate systems with tuning set results, better results can be obtained without any other language knowledge.

Coordinate System Selection for Minimum Error Rate Training in Statistical Machine Translation