Semi-supervised sub-manifold discriminant analysis
نویسندگان
چکیده
In this paper we present a semi-supervised sub-manifold discriminant analysis algorithm. To separate each sub-manifold constructed by each class, we define the within-manifold scatter, between-manifold scatter and total-manifold scatter matrices. The scatter matrices are robust to outlier and diverse-density clusters. Kernelization and direct non-linear embedding are also developed. Experimental results show that our approach can give competitive results in comparison to the state-ofthe-art algorithms.
منابع مشابه
Spectral Methods for Linear and Non-Linear Semi-Supervised Dimensionality Reduction
We present a general framework of spectral methods for semi-supervised dimensionality reduction. Applying an approach called manifold regularization, our framework naturally generalizes existent supervised frameworks. Furthermore, by our two semi-supervised versions of the representer theorem, our framework can be kernelized as well. Using our framework, we give three examples of semi-supervise...
متن کاملSemi-Supervised Based Hyperspectral Imagery Classification
Hyperspectral imagery classification is a challenging problem. Wherein, the high number of spectral channels and the high cost of true sample labeling greatly reduce the classification precision. In this paper, we proposed a semi-supervised method, which combine linear discriminant analysis and manifold learning, to improve the precision of hyperspectral imagery classification. Experimental res...
متن کاملWeb Page Classification Based on Uncorrelated Semi-Supervised Intra-View and Inter-View Manifold Discriminant Feature Extraction
Web page classification has attracted increasing research interest. It is intrinsically a multi-view and semi-supervised application, since web pages usually contain two or more types of data, such as text, hyperlinks and images, and unlabeled pages are generally much more than labeled ones. Web page data is commonly high-dimensional. Thus, how to extract useful features from this kind of data ...
متن کاملClassification by semi-supervised discriminative regularization
Linear discriminant analysis (LDA) is a well-known dimensionality reduction method which can be easily extended for data classification. Traditional LDA aims to preserve the separability of different classes and the compactness of the same class in the output space by maximizing the between-class covariance and simultaneously minimizing the within-class covariance. However, the performance of L...
متن کاملSemi-supervised Neighborhood Preserving Discriminant Embedding: A Semi-supervised Subspace Learning Algorithm
Over the last decade, supervised and unsupervised subspace learning methods, such as LDA and NPE, have been applied for face recognition. In real life applications, besides unlabeled image data, prior knowledge in the form of labeled data is also available, and can be incorporated in subspace learning algorithm resulting in improved performance. In this paper, we propose a subspace learning met...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
عنوان ژورنال:
- Pattern Recognition Letters
دوره 29 شماره
صفحات -
تاریخ انتشار 2008