Localized Feature Selection For Unsupervised Learning

نویسندگان

  • Yuanhong Li
  • YUANHONG LI
  • Xuanwen Luo
  • Yanhua Chen
  • Lijun Wang
چکیده

ACKNOWLEDGMENTS First and foremost, I want to express my greatest appreciation to my supervisor, Dr. Ming Dong. Under his guidance, I have learned a lot in different aspects of conducting research, including finding a good research topic and writing convincing technical paper. It is his guidance , support and tremendous help that made this dissertation possible. I am also very thankful to the rest of my thesis committee, including Dr. Jing hua, Dr. Reddy. Their advice and suggestions have been very helpful. and Lijun Wang for their enthusiastic help during my Ph.D. study, which I shall never forget. Let me express my special thanks to my wife, Dr. Hua Gu. Whithout her support and love, I could not complete my doctoral degree.

برای دانلود متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

ثبت نام

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

منابع مشابه

Application of Feature Selection for Unsupervised Learning in Prosecutors' Office

Feature selection is effective in removing irrelevant data. However, the result of feature selection in unsupervised learning is not as satisfying as that in supervised learning. In this paper, we propose a novel methodology ULAC (Feature Selection for Unsupervised Learning Based on Attribute Correlation Analysis and Clustering Algorithm) to identify important features for unsupervised learning...

متن کامل

Unsupervised feature selection using clustering ensembles and population based incremental learning algorithm

This paper describes a novel feature selection algorithm for unsupervised clustering, that combines the clustering ensembles method and the population based incremental learning algorithm. The main idea of the proposed unsupervised feature selection algorithm is to search for a subset of all features such that the clustering algorithm trained on this feature subset can achieve the most similar ...

متن کامل

Reconstruction-based Unsupervised Feature Selection: An Embedded Approach

Feature selection has been proven to be effective and efficient in preparing high-dimensional data for data mining and machine learning problems. Since real-world data is usually unlabeled, unsupervised feature selection has received increasing attention in recent years. Without label information, unsupervised feature selection needs alternative criteria to define feature relevance. Recently, d...

متن کامل

Online Learning of a Dirichlet Process Mixture of Generalized Dirichlet Distributions for Simultaneous Clustering and Localized Feature Selection

Online algorithms allow data instances to be processed in a sequential way, which is important for large-scale and real-time applications. In this paper, we propose a novel online clustering approach based on a Dirichlet process mixture of generalized Dirichlet (GD) distributions, which can be considered as an extension of the finite GD mixture model to the infinite case. Our approach is built ...

متن کامل

Embedded Unsupervised Feature Selection

Sparse learning has been proven to be a powerful technique in supervised feature selection, which allows to embed feature selection into the classification (or regression) problem. In recent years, increasing attention has been on applying spare learning in unsupervised feature selection. Due to the lack of label information, the vast majority of these algorithms usually generate cluster labels...

متن کامل

ذخیره در منابع من


  با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید

برای دانلود متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

ثبت نام

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

عنوان ژورنال:

دوره   شماره 

صفحات  -

تاریخ انتشار 2013