A novel discriminant minimum class locality preserving canonical correlation analysis and its applications
نویسندگان
چکیده
منابع مشابه
Visualizing Web Images Using Fisher Discriminant Locality Preserving Canonical Correlation Analysis
A novel dimensionality reduction method, Fisher Discriminant Locality Preserving Canonical Correlation Analysis (FDLPCCA), for visualizing Web images is presented in this paper. FDLP-CCA can integrate two modalities and discriminate target items in terms of their semantics by considering unique characteristics of the two modalities. In this paper, we focus on Web images with text uploaded on So...
متن کاملDiscriminant Locality Preserving Projection
In this study, we proposed an improved LPP method named Scatter-Difference Discriminant Locality Preserving Projection (SDDLPP). It considers discriminant information by maximizing the scatter-difference, which makes it have better classification capability. SDDLPP also avoids the singularity problem for the highdimensional data matrix and can be directly applied to the small sample size proble...
متن کاملCanonical Correlation Analysis on Riemannian Manifolds and Its Applications
Canonical correlation analysis (CCA) is a widely used statistical technique to capture correlations between two sets of multi-variate random variables and has found a multitude of applications in computer vision, medical imaging and machine learning. The classical formulation assumes that the data live in a pair of vector spaces which makes its use in certain important scientific domains proble...
متن کاملLocality Sensitive Discriminant Analysis
Linear Discriminant Analysis (LDA) is a popular data-analytic tool for studying the class relationship between data points. A major disadvantage of LDA is that it fails to discover the local geometrical structure of the data manifold. In this paper, we introduce a novel linear algorithm for discriminant analysis, called Locality Sensitive Discriminant Analysis (LSDA). When there is no sufficien...
متن کاملLocality Adaptive Discriminant Analysis
Linear Discriminant Analysis (LDA) is a popular technique for supervised dimensionality reduction, and its performance is satisfying when dealing with Gaussian distributed data. However, the neglect of local data structure makes LDA inapplicable to many real-world situations. So some works focus on the discriminant analysis between neighbor points, which can be easily affected by the noise in t...
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: Journal of Industrial and Management Optimization
سال: 2015
ISSN: 1547-5816
DOI: 10.3934/jimo.2016.12.251