Coupled dimensionality reduction and classification for supervised and semi-supervised multilabel learning
نویسندگان
چکیده
منابع مشابه
Coupled dimensionality reduction and classification for supervised and semi-supervised multilabel learning
Coupled training of dimensionality reduction and classification is proposed previously to improve the prediction performance for single-label problems. Following this line of research, in this paper, we first introduce a novel Bayesian method that combines linear dimensionality reduction with linear binary classification for supervised multilabel learning and present a deterministic variational...
متن کاملSemi-Supervised Dimensionality Reduction
Dimensionality reduction is among the keys in mining highdimensional data. This paper studies semi-supervised dimensionality reduction. In this setting, besides abundant unlabeled examples, domain knowledge in the form of pairwise constraints are available, which specifies whether a pair of instances belong to the same class (must-link constraints) or different classes (cannot-link constraints)...
متن کاملSemi-supervised learning in Spectral Dimensionality Reduction
Biometric face data are essentially high dimensional data and as such are susceptible to the well-known problem of the curse of dimensionality when analyzed using machine learning techniques. Various dimensionality reduction methods have been proposed in the literature to represent high dimensional data in a lower dimensional space. Research has shown that biometric face data are non-linear in ...
متن کاملBayesian Supervised Multilabel Learning with Coupled Embedding and Classification
Coupled training of dimensionality reduction and classification is proposed previously to improve the prediction performance for single-label problems. Following this line of research, in this paper, we introduce a novel Bayesian supervised multilabel learning method that combines linear dimensionality reduction with linear binary classification. We present a deterministic variational approxima...
متن کاملDimensionality reduction for supervised learning
Outline Motivation Dimensionality reduction Experimental setup Results Discussion References Outline Motivation Supervised learning High dimensionality Dimensionality reduction Principal component analysis Random projections Experimental setup Algorithms and datasets Procedure Results Discussion Outline Motivation Dimensionality reduction Experimental setup Results Discussion References Motivat...
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: Pattern Recognition Letters
سال: 2014
ISSN: 0167-8655
DOI: 10.1016/j.patrec.2013.11.021