A Fast Algorithm for Incremental Principal Component Analysis

Weng, Juyang; Zhang, Yilu; Hwang, Wonjae

doi:10.1007/978-3-540-45080-1_122

Cited by 10 publications

(7 citation statements)

References 5 publications

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“…In the future, instead of down-sampling, we will investigate advanced methods, such as incremental multilinear PCA [30]. The quaternion matrix is first converted to a quaternion vector recorded by row.…”

Section: The Framework Of the Proposed Methodsmentioning

confidence: 99%

“…To speed up computation and reduce memory cost [ 8 ], the ROIs are downsampled to 64 × 64 as shown in Figure 5 . In the future, instead of down-sampling, we will investigate advanced methods, such as incremental multilinear PCA [ 30 ]. The quaternion matrix is first converted to a quaternion vector recorded by row.…”

Section: The Framework Of the Proposed Methodsmentioning

confidence: 99%

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Multispectral Palmprint Recognition Using a Quaternion Matrix

Guo

Song

et al. 2012

Sensors

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Palmprints have been widely studied for biometric recognition for many years. Traditionally, a white light source is used for illumination. Recently, multispectral imaging has drawn attention because of its high recognition accuracy. Multispectral palmprint systems can provide more discriminant information under different illuminations in a short time, thus they can achieve better recognition accuracy. Previously, multispectral palmprint images were taken as a kind of multi-modal biometrics, and the fusion scheme on the image level or matching score level was used. However, some spectral information will be lost during image level or matching score level fusion. In this study, we propose a new method for multispectral images based on a quaternion model which could fully utilize the multispectral information. Firstly, multispectral palmprint images captured under red, green, blue and near-infrared (NIR) illuminations were represented by a quaternion matrix, then principal component analysis (PCA) and discrete wavelet transform (DWT) were applied respectively on the matrix to extract palmprint features. After that, Euclidean distance was used to measure the dissimilarity between different features. Finally, the sum of two distances and the nearest neighborhood classifier were employed for recognition decision. Experimental results showed that using the quaternion matrix can achieve a higher recognition rate. Given 3000 test samples from 500 palms, the recognition rate can be as high as 98.83%.

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Section: The Framework Of the Proposed Methodsmentioning

confidence: 99%

Section: The Framework Of the Proposed Methodsmentioning

confidence: 99%

Multispectral Palmprint Recognition Using a Quaternion Matrix

Guo

Song

et al. 2012

Sensors

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show abstract

“…Since the new samples are added one by one and the least significant principal components are discarded to preserve the dimensions of the subspaces, the method also suffers from the problem of unpredicted approximation error. The second category can estimate the principal components without directly computing the covariance matrix [24,25]. The candid covariance-free IPCA (CCIPCA) algorithm developed by Weng et al [24] can estimate the principal components of the high-dimensional image matrices quickly with a good convergence performance.…”

Section: Introductionmentioning

confidence: 99%

An Incremental Two-Dimensional Principal Component Analysis for Object Recognition

Sun

2018

Mathematical Problems in Engineering

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Two-dimensional principal component analysis algorithm (2DPCA) can be performed in the batch mode and can not meet the real-time requirements of the video stream. To overcome these limitations, the incremental learning of the candid covariancefree incremental PCA (CCIPCA) is innovated to the existing 2DPCA, and the called incremental 2DPCA (I2DPCA) is firstly presented to incrementally compute the principal components of a sequence of samples directly on the 2D image matrices without estimating the covariance matrices. Therefore, the I2DPCA can improve the feature extraction speed and reduce the required memory. However, the variations between the column direction, generally neglected, are also useful for the high-accuracy object recognition. Thus, another incremental sequential row-column 2DPCA algorithm (IRC2DPCA), based on the proposed I2DPCA algorithm, is also proposed. The IRC2DPCA can compress the image matrices in the row and column direction, and the feature matrices extracted by the IRC2DPCA are with less dimensions than the I2DPCA. The substantial experimental results show that the IRC2DPCA, compared with the other three algorithms, can improve the convergence rates and the recognition rates, compress the dimensions of the feature matrices, and reduce the feature extraction time and the classification time.

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“…Their popularity [1,[3][4][5]8,11,[13][14][15]19,[22][23][24] is due to many reasons like low cost hardware implementation, low memory requirement, less processing time and less computational complexity.…”

Section: Introductionmentioning

confidence: 99%

“…The fixed-point algorithm of PCA is also known as fast PCA (FPCA) algorithm. The FPCA algorithm has been recently extended and applied in face recognition [8,14,15], communication [13,24], VLSI architecture design [3][4][5] and in other areas or applications like in Yang et al [23]; Shi and Guo [19]; Lai and Huang [11]; Wang et al [22]; Albanese et al [1]. The feature selection method plays a significant role in identifying crucial genes related to human cancers.…”

Section: Introductionmentioning

confidence: 99%

A feature selection method using fixed-point algorithm for DNA microarray gene expression data

Sharma

Paliwal²,

Imoto

et al. 2014

KES

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As the performance of hardware is limited, the focus has been to develop objective, optimized and computationally efficient algorithms for a given task. To this extent, fixed-point and approximate algorithms have been developed and successfully applied in many areas of research. In this paper we propose a feature selection method based on fixed-point algorithm and show its application in the field of human cancer classification using DNA microarray gene expression data. In the fixed-point algorithm, we utilize between-class scatter matrix to compute the leading eigenvector. This eigenvector has been used to select genes. In the computation of the eigenvector, the eigenvalue decomposition of the scatter matrix is not required which significantly reduces its computational complexity and memory requirement.

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A Fast Algorithm for Incremental Principal Component Analysis

Cited by 10 publications

References 5 publications

Multispectral Palmprint Recognition Using a Quaternion Matrix

Multispectral Palmprint Recognition Using a Quaternion Matrix

An Incremental Two-Dimensional Principal Component Analysis for Object Recognition

A feature selection method using fixed-point algorithm for DNA microarray gene expression data

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