Globality-Locality Preserving Maximum Variance Extreme Learning Machine
نویسندگان
چکیده
منابع مشابه
A Novel Support Vector Machine with Globality-Locality Preserving
Support vector machine (SVM) is regarded as a powerful method for pattern classification. However, the solution of the primal optimal model of SVM is susceptible for class distribution and may result in a nonrobust solution. In order to overcome this shortcoming, an improved model, support vector machine with globality-locality preserving (GLPSVM), is proposed. It introduces globality-locality ...
متن کاملFace Recognition via Globality-Locality Preserving Projections
We present an improved Locality Preserving Projections (LPP) method, named Gloablity-Locality Preserving Projections (GLPP), to preserve both the global and local geometric structures of data. In our approach, an additional constraint of the geometry of classes is imposed to the objective function of conventional LPP for respecting some more global manifold structures. Moreover, we formulate a ...
متن کاملMaximum Margin Clustering Using Extreme Learning Machine
Maximum margin clustering (MMC) is a newly proposed clustering method, which extends large margin computation of support vector machine (SVM) to unsupervised learning. But in nonlinear cases, time complexity is still high. Since extreme learning machine (ELM) has achieved similar generalization performance at much faster learning speed than traditional SVM and LS-SVM, we propose an extreme maxi...
متن کاملLocality Preserving Feature Learning
Locality Preserving Indexing (LPI) has been quite successful in tackling document analysis problems, such as clustering or classification. The approach relies on the Locality Preserving Criterion, which preserves the locality of the data points. However, LPI takes every word in a data corpus into account, even though many words may not be useful for document clustering. To overcome this problem...
متن کاملLearning Locality-Preserving Discriminative Features
This paper describes a novel framework for learning discriminative features, where both labeled and unlabeled data are used to map the data instances to a lower dimensional space, preserving both class separability and data manifold topology. In contrast to linear discriminant analysis (LDA) and its variants (like semi-supervised discriminant analysis), which can only return c−1 features for a ...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Complexity
سال: 2019
ISSN: 1076-2787,1099-0526
DOI: 10.1155/2019/1806314