资源分类
》 资源 》论文》Knowledge Driven Dimension Reduction For Clustering

Knowledge Driven Dimension Reduction For Clustering

2019-11-15 | |  56 |   35 |   0

Abstract As A.I. algorithms are applied to more complex domains that involve high dimensional data sets there is a need to more saliently represent the data. However, most dimension reduction approaches are driven by objective functions that may not or only partially suit the end users requirements. In this work, we show how to incorporate general-purpose domain expertise encoded as a graph into dimension reduction in way that lends itself to an elegant generalized eigenvalue problem. We call our approach Graph-Driven Constrained Dimension Reduction via Linear Projection (GCDR-LP) and show that it has several desirable properties.

上一篇：Bayesian Extreme Components Analysis

下一篇：Search Techniques for Fourier-Based Learning

用户评价
全部评价

热门资源

  • The Variational S...

    Unlike traditional images which do not offer in...

  • Learning to Predi...

    Much of model-based reinforcement learning invo...

  • Stratified Strate...

    In this paper we introduce Stratified Strategy ...

  • A Mathematical Mo...

    Direct democracy, where each voter casts one vo...

  • Rating-Boosted La...

    The performance of a recommendation system reli...