Local Feature Extraction and Its Applications Using a Library of Bases

Saito, Naoki

doi:10.1142/9789812813305_0005

Cited by 100 publications

(100 citation statements)

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“…There are in fact more than 2 ⌊N/2⌋ possible bases choosable from this overcomplete set, which allow us to select a basis most suitable for the task at hand via the best-basis type algorithms originally developed for regularly-sampled signals; see e.g., [13].…”

Section: Several Remarks On This Algorithm Are In Ordermentioning

confidence: 99%

Hierarchical graph Laplacian eigen transforms

Irion¹,

Saito²

2014

JSIAM Letters

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We describe a new transform that generates a dictionary of bases for handling data on a graph by combining recursive partitioning of the graph and the Laplacian eigenvectors of each subgraph. Similar to the wavelet packet and local cosine dictionaries for regularly sampled signals, this dictionary of bases on the graph allows one to select an orthonormal basis that is most suitable to one's task at hand using a best-basis type algorithm. We also describe a few related transforms including a version of the Haar wavelet transform on a graph, each of which may be useful in its own right.

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Section: Several Remarks On This Algorithm Are In Ordermentioning

confidence: 99%

Hierarchical graph Laplacian eigen transforms

Irion¹,

Saito²

2014

JSIAM Letters

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“…To show the associative property, we use the Cylinder-Bell-Funnel (CBF) [17], Leaf, Face, Gun, and ECG dataset, from the UCR time series data mining archive [http://www.cs.ucr.edu/~eamonn/time_series_data/]. The well-known 3-class CBF dataset contains 64 instances with the length of 128 data points.…”

Section: Does Reordering Make Any Differences?mentioning

confidence: 99%

Inaccuracies of Shape Averaging Method Using Dynamic Time Warping for Time Series Data

Niennattrakul

Ratanamahatana

2007

Computational Science – ICCS 2007

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Abstract.Shape averaging or signal averaging of time series data is one of the prevalent subroutines in data mining tasks, where Dynamic Time Warping distance measure (DTW) is known to work exceptionally well with these time series data, and has long been demonstrated in various data mining tasks involving shape similarity among various domains. Therefore, DTW has been used to find the average shape of two time series according to the optimal mapping between them. Several methods have been proposed, some of which require the number of time series being averaged to be a power of two. In this work, we will demonstrate that these proposed methods cannot produce the real average of the time series. We conclude with a suggestion of a method to potentially find the shape-based time series average.

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“…CBF The CBF data has been introduced by Saito [87] as an artificial problem. The learning task is to distinguish the data from the three classes, including cylinder(c), bell(b) and funnel(f).…”

Section: Time Series Data Setsmentioning

confidence: 99%