Detailed Instructions: 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.
Q: In this paper, we propose an extremely simple deep model for the unsupervised nonlinear dimensionality reduction deep distributed random samplings. First, its network structure is novel: each layer of the network is a group of mutually independent k-centers clusterings. Second, its learning method is extremely simple: the k centers of each clustering are only k randomly selected examples from the training data; for small-scale data sets, the k centers are further randomly reconstructed by a simple cyclic-shift operation. Experimental results on nonlinear dimensionality reduction show that the proposed method can learn abstract representations on both large-scale and small-scale problems, and meanwhile is much faster than deep neural networks on large-scale problems.
A:
Learning Deep Representations By Distributed Random Samplings