Principal component analysis on vector computers

Heras, Dora B.; Dominguez, Jessica A.; Blanco, Vicente; Costas, Pablo; Rivera, Francisco F.

doi:10.1007/3-540-62828-2_133

Cited by 6 publications

(4 citation statements)

References 9 publications

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“…For example, RP (random projection) (Buhler & Tompa, 2001), PCA (Principal Component Analysis) (Heras et al, 1996), ICA (Independent Component Analysis) (Hyvrinen & Oja, 2000) are popular methods. First, we focus on RP and PCA.…”

Section: Different Methods Of Transformationmentioning

confidence: 99%

“…But, PCA determines the order of principal components by eigenvalues calculated from eigenvectors of original data. The first principal component maintains the largest amount of information of the original data in all principal components (Heras et al, 1996). On the other hand, it is known that the order of independent components is random (Hyvrinen & Oja, 2000).…”

Section: Different Methods Of Transformationmentioning

confidence: 99%

“…In addition, the first principal component maintains the largest amount of information of the original data (Heras et al, 1996). So, we can say that the first principal component is a linear combination of the original variables weighted according to the contribution in the original data.…”

Section: Principal Component Analysismentioning

confidence: 99%

“…Among them, our previous algorithm (Tanaka & Uehara, 2003) can extract motifs from multidimensional time-series data. First, the motif discovery algorithm transforms multidimensional time-series data into 1-dimensional time-series data by using PCA (Principal Component Analysis) (Heras et al, 1996) for reducing dimensions of the data. Then, to extract patterns that have appeared in the 1-dimensional time-series data, we transform the data into a sequence of symbols.…”

Section: Introductionmentioning

confidence: 99%

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Discovery of Time-Series Motif from Multi-Dimensional Data Based on MDL Principle

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Abstract.Recently, the research on efficient extraction of previously unknown, frequently appearing patterns in a time-series data has received much attention. These patterns are called 'motifs'. Motifs are useful for various time-series data mining tasks. In this paper, we propose a motif discovery algorithm to extract a motif that represents a characteristic pattern of the given data based on Minimum Description Length (MDL) principle. In addition, the algorithm can extract motifs from multi-dimensional time-series data by using Principal Component Analysis (PCA). In experimental evaluation, we show the efficiency of the motif discovery algorithm, and the usefulness of extracted motifs to various data mining tasks.

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Section: Different Methods Of Transformationmentioning

confidence: 99%

Section: Different Methods Of Transformationmentioning

confidence: 99%

Section: Principal Component Analysismentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Discovery of Time-Series Motif from Multi-Dimensional Data Based on MDL Principle

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Hardware Acceleration of EDA Algorithms

Gulati¹,

Khatri²

2010

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Accelerating statistical static Timing Analysis Using Graphics Processors

Gulati¹,

Khatri

2010

Hardware Acceleration of EDA Algorithms

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Principal component analysis on vector computers

Cited by 6 publications

References 9 publications

Discovery of Time-Series Motif from Multi-Dimensional Data Based on MDL Principle

Discovery of Time-Series Motif from Multi-Dimensional Data Based on MDL Principle

Hardware Acceleration of EDA Algorithms

Accelerating statistical static Timing Analysis Using Graphics Processors

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