Graph-based semi-supervised learning with multiple labels

نویسندگان

  • Zheng-Jun Zha
  • Tao Mei
  • Jingdong Wang
  • Zengfu Wang
  • Xian-Sheng Hua
چکیده

Conventional graph-based semi-supervised learning methods predominantly focus on single label problem. However, it is more popular in real-world applications that an example is associated with multiple labels simultaneously. In this paper, we propose a novel graph-based learning framework in the setting of semi-supervised learning with multiple labels. This framework is characterized by simultaneously exploiting the inherent correlations among multiple labels and the label consistency over the graph. Based on the proposed framework, we further develop two novel graph-based algorithms. We apply the proposed methods to video concept detection over TRECVID 2006 corpus and report superior performance compared to the state-of-the-art graph-based approaches and the representative semi-supervised multi-label learning methods.

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

Semi-Supervised Learning on an Augmented Graph with Class Labels

In this paper, we propose a novel graph-based method for semi-supervised learning. Our method runs a diffusion-based affinity learning algorithm on an augmented graph consisting of not only the nodes of labeled and unlabeled data but also artificial nodes representing class labels. The learned affinities between unlabeled data and class labels are used for classification. Our method achieves su...

متن کامل

Semi-supervised 3D model multiple semantic automatic annotation

the purpose of annotation for 3D model is that it can automatically list the best suitable labels to describe the 3D models; it is an important part of the text-based 3D model retrieval. The existence of the semantic gap makes the result based on the similarity matching techniques needs to be improved. In order to improve the 3D model annotation performance using a large number of unlabeled sam...

متن کامل

Instance-Level Label Propagation with Multi-Instance Learning

Label propagation is a popular semi-supervised learning technique that transfers information from labeled examples to unlabeled examples through a graph. Most label propagation methods construct a graph based on example-to-example similarity, assuming that the resulting graph connects examples that share similar labels. Unfortunately, examplelevel similarity is sometimes badly defined. For inst...

متن کامل

The Un-normalized Graph p-Laplacian based Semi-supervised Learning Method and Speech Recognition Problem

Speech recognition is the classical problem in pattern recognition research field. However, just a few graph based machine learning methods have been applied to this classical problem. In this paper, we propose the un-normalized graph p-Laplacian semi-supervised learning methods and these methods will be applied to the speech network constructed from the MFCC speech dataset to predict the label...

متن کامل

Bidirectional Semi-supervised Learning with Graphs

We present a machine learning task, which we call bidirectional semi-supervised learning, where label-only samples are given as well as labeled and unlabeled samples. A label-only sample contains the label information of the sample but not the feature information. Then, we propose a simple and effective graph-based method for bidirectional semisupervised learning in multi-label classification. ...

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:
  • J. Visual Communication and Image Representation

دوره 20  شماره 

صفحات  -

تاریخ انتشار 2009