Supervised Fractional-Order Embedding Multiview Canonical Correlation Analysis via Ordinal Label Dequantization for Image Interest Estimation
نویسندگان
چکیده
منابع مشابه
Semi-Supervised, Dimensionality Reduction via Canonical Correlation Analysis
We analyze the multi-view regression problemwhere we have two views (X1, X2) of the input data and a real target variable Y of interest. In a semi-supervised learning setting, we consider two separate assumptions (one based on redundancy and the other based on (de)correlation) and show how, under either assumption alone, dimensionality reduction (based on CCA) could reduce the labeled sample co...
متن کاملCanonical Correlation Analysis for Multiview Semisupervised Feature Extraction
Hotelling’s Canonical Correlation Analysis (CCA) works with two sets of related variables, also called views, and its goal is to find their linear projections with maximal mutual correlation. CCA is most suitable for unsupervised feature extraction when given two views but it has been also long known that in supervised learning when there is only a single view of data given, the supervision sig...
متن کاملCross-Modal Image Clustering via Canonical Correlation Analysis
A new algorithm via Canonical Correlation Analysis (CCA) is developed in this paper to support more effective crossmodal image clustering for large-scale annotated image collections. It can be treated as a bi-media multimodal mapping problem and modeled as a correlation distribution over multimodal feature representations. It integrates the multimodal feature generation with the Locality Linear...
متن کاملStability analysis of fractional-order nonlinear Systems via Lyapunov method
In this paper, we study stability of fractional-order nonlinear dynamic systems by means of Lyapunov method. To examine the obtained results, we employe the developed techniques on test examples.
متن کاملSupervised and Semi-Supervised Multi-View Canonical Correlation Analysis Ensemble for Heterogeneous Domain Adaptation in Remote Sensing Image Classification
In this paper, we present the supervised multi-view canonical correlation analysis ensemble (SMVCCAE) and its semi-supervised version (SSMVCCAE), which are novel techniques designed to address heterogeneous domain adaptation problems, i.e., situations in which the data to be processed and recognized are collected from different heterogeneous domains. Specifically, the multi-view canonical corre...
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: IEEE Access
سال: 2021
ISSN: 2169-3536
DOI: 10.1109/access.2021.3055868