Principal Component Analysis

Du, Ke-Lin; Swamy, M. N. S.

doi:10.1007/978-1-4471-5571-3_12

Cited by 8 publications

(6 citation statements)

References 134 publications

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“…We have selected PCA as the competing method to evaluate the performance of the proposed EPA approach. PCA is a classical linear transformation which transforms the original features to principal components (PCs), hence achieves effective dimension reduction (Du and Swamy, 2014).…”

Section: Competing Methods and Discussionmentioning

confidence: 99%

Elliptical modeling and pattern analysis for perturbation models and classification

Suthaharan

Shen

2018

Int J Data Sci Anal

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The characteristics (or numerical patterns) of a feature vector in the transform domain of a perturbation model differ significantly from those of its corresponding feature vector in the input domain.These differences -caused by the perturbation techniques used for the transformation of feature patterns -degrade the performance of machine learning techniques in the transform domain. In this paper, we proposed a nonlinear parametric perturbation model that transforms the input feature patterns to a set of elliptical patterns, and studied the performance degradation issues associated with random forest classification technique using both the input and transform domain features.Compared with the linear transformation such as Principal Component Analysis (PCA), the proposed method requires less statistical assumptions and is highly suitable for the applications such as data privacy and security due to the difficulty of inverting the elliptical patterns from the transform domain to the input domain. In addition, we adopted a flexible block-wise dimensionality reduction step in the proposed method to accommodate the possible high-dimensional data in modern applications. We evaluated the empirical performance of the proposed method on a network intrusion data set and a biological data set, and compared the results with PCA in terms of classification performance and data privacy protection (measured by the blind source separation attack and signal interference ratio). Both results confirmed the superior performance of the proposed elliptical transformation.

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Section: Competing Methods and Discussionmentioning

confidence: 99%

Elliptical modeling and pattern analysis for perturbation models and classification

Suthaharan

Shen

2018

Int J Data Sci Anal

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“…Locality Sensitive Hashing (LSH) and its variants [22,23,24] are representative unsupervised hashing methods which generate hash functions in a random manner. Based on the data distribution information of images, Principal Component Hashing (PCH) [25] trains hash functions by principal component analysis [26] and utilizes the top-K principal components of the covariance matrix to construct its hashing projections. Iterative Quantization Hashing (ITQ) [12] learns the optimal rotation matrix for the data after the principle component analysis by minimizing the quantization error.…”

Section: Hashing Methods With Single Hash Tablementioning

confidence: 99%

Bootstrap dual complementary hashing with semi-supervised re-ranking for image retrieval

Tian

Xian-cheng

et al. 2020

Neurocomputing

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With the rapid growth of multimedia data on the Internet, content-based image retrieval becomes a key technique for the Internet development. Hashing methods are e cient and e↵ective for image retrieval. Dual Complementary Hashing (DCH) is one such method, which uses multiple hash tables and has good performance. However, DCH utilizes wrongly hashed image pairs to train the following hash table and discards correctly hashed image pairs. Therefore, the number of image pairs utilized for training the following hash tables will decrease rapidly. Moreover, each hash function in a hash table of DCH is trained by correcting the errors caused by its preceding one instead of holistically considering errors made by all previous hash functions. These restrictions significantly reduce the training e ciency and the overall performance of DCH.In this paper, we propose a new hashing method for image retrieval, Bootstrap Dual Complementary Hashing with semi-supervised Re-ranking (BDCHR). It is a semi-supervised multi-hashing method consisting of two parts: bootstrap DCH and semi-supervised re-ranking. The first part relieves the restrictions of DCH while the second part further enhances the image retrieval performance.Experimental results show that BDCHR yields better performance than other state-of-the-art multi-hashing methods.

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“…Finally, the visualization method presented here is in fact a supervised learning algorithm, like supervised PCA (Koren and Carmel, 2004;Yu et al, 2006;Du et al, 2015) for instance. The difference is that our approach explicitly incorporates the loss function and the definition of similarity that we want to obtain at the end of the process.…”

Section: Related Workmentioning

confidence: 99%

Mapping preferences into Euclidean space

Luaces

Díez

Joachims

et al. 2015

Expert Systems with Applications

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Understanding and modeling human preferences is one of the key problems in applications ranging from marketing to automated recommendation. In this paper, we focus on learning and analyzing the preferences of consumers regarding food products. In particular, we explore machine learning methods that embed consumers and products in an Euclidean space such that their relationship to each other models consumer preferences. In addition to predicting preferences that were not explicitly stated, the Euclidean embedding enables visualization and clustering to understand the overall structure of a population of consumers and their preferences regarding the set of products.Notice that consumers' clusters are market segments, and products clusters can be seen as groups of similar items with respect to consumer tastes. We explore two types of Euclidean embedding of preferences, one based on inner products and one based on distances. Using a real world dataset about consumers of beef meat, we find that both embeddings produce more accurate models than a tensorial approach that uses a SVM to learn preferences. The * Corresponding author: Tel: +34 985 182 588Email addresses: oluaces@uniovi.es (Oscar Luaces), jdiez@uniovi.es (Jorge Díez), tj@cs.cornell.edu (Thorsten Joachims), abahamonde@uniovi.es (Antonio Bahamonde) Preprint submitted to Expert Systems with ApplicationsJuly 15, 2015 reason is that the number of parameters to learned in embeddings can be considerably lower than in the tensorial approach. Furthermore, we demonstrate that the visualization of the learned embeddings provides interesting insights into the structure of the consumer and product space, and that it provides a method for qualitatively explaining consumer preferences. Additionally, it is important to emphasize that the approach presented here is flexible enough to allow its use with different levels of knowledge about consumers or products; therefore the application field is very wide to grasp an accurate understanding of consumers' preferences.

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Principal Component Analysis

Cited by 8 publications

References 134 publications

Elliptical modeling and pattern analysis for perturbation models and classification

Elliptical modeling and pattern analysis for perturbation models and classification

Bootstrap dual complementary hashing with semi-supervised re-ranking for image retrieval

Mapping preferences into Euclidean space

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