Clustering and Active Learning Using a LSI Subspace

نویسندگان

  • Weizhong Zhu
  • Christopher C. Yang
  • Guangrong Sun
  • Hongming Cui
  • Fengying Li
  • Weihong Cui
چکیده

.......................................................................................................... xiv CHAPTER1: Introduction......................................................................................... 1 1.1 Latent Semantic Indexing .......................................................................... 4 1.2 Visual Exploration of the LSI Subspaces.................................................. 8 1.3 Research Questions................................................................................... 13 1.4 Organization of the Thesis ....................................................................... 15 CHAPTER2: Literature Survey.............................................................................. 16 2.1 Data Models for Semantic Content Representation .............................. 16 2.2 Text Clustering.......................................................................................... 21 2.3 Active Learning......................................................................................... 23 2.4 Query Expansion....................................................................................... 25 2.5 Social Network Analysis ........................................................................... 27 CHAPTER3: The LSI Subspace Signature Model ................................................ 29 3.1 LSI Subspace Term Signatures and Document Signatures .................. 31 3.2 LSI Subspace Signature Ranking............................................................ 35 3.2.

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

Document clustering using the LSI subspace signature model

We describe the Latent Semantic Indexing Subspace Signature Model (LSISSM) for semantic content representation of unstructured text. Grounded on Singular Value Decomposition (SVD), the model represents terms and documents by the distribution signatures of their statistical contribution across the topranking latent concept dimensions. LSISSM matches term signatures with document signatures accor...

متن کامل

High-Dimensional Unsupervised Active Learning Method

In this work, a hierarchical ensemble of projected clustering algorithm for high-dimensional data is proposed. The basic concept of the algorithm is based on the active learning method (ALM) which is a fuzzy learning scheme, inspired by some behavioral features of human brain functionality. High-dimensional unsupervised active learning method (HUALM) is a clustering algorithm which blurs the da...

متن کامل

An Improved Semi-supervised Clustering Algorithm Based on Active Learning

In order to solve the difficult questions such as in the presence of the cluster deviation and high dimensional data processing in traditional semi-supervised clustering algorithm, a semi-supervised clustering algorithm based on active learning was proposed, this algorithm can effectively solve the above two problems. Using active learning strategies in algorithm can obtain a large amount of in...

متن کامل

Learning Robust Subspace Clustering

We propose a low-rank transformation-learning framework to robustify subspace clustering. Many high-dimensional data, such as face images and motion sequences, lie in a union of low-dimensional subspaces. The subspace clustering problem has been extensively studied in the literature to partition such highdimensional data into clusters corresponding to their underlying low-dimensional subspaces....

متن کامل

Learning Transformations for Clustering and Classification Learning Transformations for Clustering and Classification

A low-rank transformation learning framework for subspace clustering and classification is here proposed. Many high-dimensional data, such as face images and motion sequences, approximately lie in a union of low-dimensional subspaces. The corresponding subspace clustering problem has been extensively studied in the literature to partition such highdimensional data into clusters corresponding to...

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 2009