Locality Preserving Canonical Correlation Analysis Research Articles

Multiset Canonical Correlations Analysis With Global Structure Preservation

This paper considers unsupervised dimensionality reduction of multi-view data, where locality preserving canonical correlation analysis and a new locality-preserving canonical correlation analysis are two typical effective methods. However, they ignore the global structure while considering the local structure of data, and are sensitive to noises because of the relationship of neighbors based on the Euclidean distance. In this paper, we propose a novel multi-view dimensionality reduction method: multiset canonical correlations analysis based on low-rank representation. Our model introduces the cross-view similarity matrix to consider the correlation of all different points in cross views, which makes it not only preserve the local structure but also the global structure of data. And the cross-view similarity matrix is constructed by using low-rank representation, which can make the model more robust. In addition, a parameter $\beta $ is introduced to adjust the importance of the correlation of different sample points, enhancing the generalization ability for different datasets. Experiments on four multi-view datasets show our proposed method has better performance than the related methods.

Novel Audio Feature Projection Using KDLPCCA-Based Correlation with EEG Features for Favorite Music Classification

A novel audio feature projection using Kernel Discriminative Locality Preserving Canonical Correlation Analysis (KDLPCCA)-based correlation with electroencephalogram (EEG) features for favorite music classification is presented in this paper. The projected audio features reflect individual music preference adaptively since they are calculated by considering correlations with the user's EEG signals during listening to musical pieces that the user likes/dislikes via a novel CCA proposed in this paper. The novel CCA, called KDLPCCA, can consider not only a non-linear correlation but also local properties and discriminative information of each class sample, namely, music likes/dislikes. Specifically, local properties reflect intrinsic data structures of the original audio features, and discriminative information enhances the power of the final classification. Hence, the projected audio features have an optimal correlation with individual music preference reflected in the user's EEG signals, adaptively. If the KDLPCCA-based projection that can transform original audio features into novel audio features is calculated once, our method can extract projected audio features from a new musical piece without newly observing individual EEG signals. Our method therefore has a high level of practicability. Consequently, effective classification of user's favorite musical pieces via a Support Vector Machine (SVM) classifier using the new projected audio features becomes feasible. Experimental results show that our method for favorite music classification using projected audio features via the novel CCA outperforms methods using original audio features, EEG features and even audio features projected by other state-of-the-art CCAs.

Visualizing Web Images Using Fisher Discriminant Locality Preserving Canonical Correlation Analysis

A novel dimensionality reduction method, Fisher Discriminant Locality Preserving Canonical Correlation Analysis (FDLP-CCA), 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 Social Networking Services for these two modalities. Specifically, text features have high discriminate power in terms of semantics. On the other hand, visual features of images give their perceptual relationships. In order to consider both of the above unique characteristics of these two modalities, FDLP-CCA estimates the correlation between the text and visual features with consideration of the cluster structure based on the text features and the local structures based on the visual features. Thus, FDLP-CCA can integrate the different modalities and provide separated manifolds to organize enhanced compactness within each natural cluster.

Globalized and localized canonical correlation analysis with multiple empirical kernel mapping

Canonical Correlation Analysis (CCA) reveals linear correlation relationship between two feature sets, but fails to discover nonlinear relationship. Kernel CCA (KCCA) overcomes such a shortcoming. Unfortunately, both of them fail to discover local structure of features whereas Locality Preserving CCA (LPCCA) possesses this ability. It is found that LPCCA ignores relationship between global and local structures of features. Moreover, these CCA-based methods have no ability to deal with single-view data which only has single feature set. To this end, we apply Multiple Explicitly Kernel Mapping (MEKM) to the application at first and take global and local structures of features into account. The proposed method is named Globalized and Localized CCA with MEKM (GLCCA-MEKM). Experiments validate that (i) introducing MEKM can map original features into multiple feature spaces so that multiple feature sets of data are obtained. Further in these feature spaces, nonlinear correlation relationship between features are also gotten; (ii) taking global and local structures of features into account makes the mapped features keep both original global and local properties. These processes make GLCCA-MEKM possess the ability to deal with single-view data and be locality-preserving. Therefore, GLCCA-MEKM has below contributions. First, GLCCA-MEKM can inherit the advantages of traditional MEKM, deal with single-view data, and reveal nonlinear correlation relationship between two feature sets. Second, GLCCA-MEKM extracts both global and local structural information more reasonably and coordinates their relationship well. In doing so, mapped features can keep original global and local properties. Finally, classifiers with GLCCA-MEKM obtain better classification performances.

A New Locality-Preserving Canonical Correlation Analysis Algorithm for Multi-View Dimensionality Reduction

Canonical correlation analysis (CCA) is a well-known technique for extracting linearly correlated features from multiple views (i.e., sets of features) of data. Recently, a locality-preserving CCA,...

Localization in Wireless Sensor Network Using Locality Preserving Canonical Correlation Analysis

In this paper,the deployment of wireless network is analyzed in accordance with the manifold distribution.Next,the previous Locality Preserving Canonical Correlation Analysis(LPCCA) algorithm is used to build a mapping from the signal-vector space to the physical location space and develop a location algorithm,Location Estimation-Locality Preserving Canonical Correlation Analysis(LE-LPCCA,for short),which sufficiently takes the local structure of the network topology into account.Finally,experimental results on benchmark show that this method achieves a higher accuracy and stability than other location algorithms.

Manifold‐based canonical correlation analysis for wireless sensor network localization

ABSTRACTSignal‐strength‐based location estimation in wireless sensor networks is to locate the physical positions of unknown sensors via the received signal strengths. In this field, there are few localization researches sufficiently exploiting topology structures of the network in both signal space and physical space. The goal of this paper is to first establish two effective localization models based on specific manifold (or local) structures of both signal space and physical (location) space by using our previous locality preserving canonical correlation analysis (LPCCA) model and a newly‐proposed locality correlation analysis (LCA) model, and then develop their corresponding novel location algorithms, called location estimation—LPCCA (LE—LPCCA) and location estimation—LCA (LE—LCA). Since both LPCCA and LCA relatively sufficiently take into account locality characteristics of the manifold structures in both the spaces, our localization algorithms developed from them consequently achieve better localization accuracy than other publicly available advanced algorithms. Copyright © 2011 John Wiley & Sons, Ltd.