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Results 1 - 10
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80,140
low-dimensional spaces
"... By Hyun Joon Shin ∗ and Jehee Lee Human motion is difficult to create and manipulate because of the high dimensionality and spatiotemporal nature of human motion data. Recently, the use of large collections of captured motion data has added increased realism in character animation. In order to make ..."
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the synthesis and analysis of motion data tractable, we present a low-dimensional motion space in which high-dimensional human motion can be effectively visualized, synthesized, edited, parameterized, and interpolated in both spatial and temporal domains. Our system allows users to create and edit the motion
Embedding ultrametrics into low-dimensional spaces
, 2006
"... We study the problem of minimum-distortion embedding of ultrametrics into the plane and higher dimensional spaces. Ultrametrics are a natural class of metrics that frequently occur in applications involving hierarchical clustering. Low-distortion embeddings of ultrametrics into the plane help visual ..."
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Cited by 5 (5 self)
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We study the problem of minimum-distortion embedding of ultrametrics into the plane and higher dimensional spaces. Ultrametrics are a natural class of metrics that frequently occur in applications involving hierarchical clustering. Low-distortion embeddings of ultrametrics into the plane help
The Characterisation of Chaos in Low Dimensional Spaces
, 1983
"... This thesis is made available online and is protected by original copyright. Please scroll down to view the document itself. Please refer to the repository record for this item for information to help you to cite it. Our policy information is available from the repository home page. ..."
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This thesis is made available online and is protected by original copyright. Please scroll down to view the document itself. Please refer to the repository record for this item for information to help you to cite it. Our policy information is available from the repository home page.
Physically-based Character Control in Low Dimensional Space
"... Abstract. In this paper, we propose a new method to compose physicallybased character controllers in low dimensional latent space. Source controllers are created by gradually updating the task parameter such as the external force applied to the body. During the optimization, instead of only saving ..."
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Abstract. In this paper, we propose a new method to compose physicallybased character controllers in low dimensional latent space. Source controllers are created by gradually updating the task parameter such as the external force applied to the body. During the optimization, instead of only saving
Audio Key Finding Using Low-Dimensional Spaces
"... This paper presents two models of audio key finding: a template based correlational model and a template based model that uses a low-dimensional tonal representation. The first model uses a confidence weighted correlation to find the most probable key. The second model is distance based and employs ..."
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This paper presents two models of audio key finding: a template based correlational model and a template based model that uses a low-dimensional tonal representation. The first model uses a confidence weighted correlation to find the most probable key. The second model is distance based and employs
Approximation Algorithms for Low-Distortion Embeddings Into Low-Dimensional Spaces
- in Proceedings of the 16th Annual ACMSIAM Symposium on Discrete Algorithms
, 2005
"... Abstract We present several approximation algorithms for theproblem of embedding metric spaces into a line, and into the two-dimensional plane. Among other results, wegive an O(pn)-approximation algorithm for the prob-lem of finding a line embedding of a metric induced by a given unweighted graph, t ..."
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Cited by 28 (9 self)
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, that minimizes the (standard)multiplicative distortion. We give an improved ~ O(n1/3)approximation for the case of metrics generated by unweighted trees. This is the first result of this type. 1 Introduction Embedding distance matrices into geometric spaces(most notably, into low-dimensional spaces) is a
On Low Distortion Embeddings of Statistical Distance Measures into Low Dimensional Spaces ∗
, 909
"... Statistical distance measures have found wide applicability in information retrieval tasks that typically involve high dimensional datasets. In order to reduce the storage space and ensure efficient performance of queries, dimensionality reduction while preserving the inter-point similarity is highl ..."
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Cited by 2 (0 self)
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is highly desirable. In this paper, we investigate various statistical distance measures from the point of view of discovering low distortion embeddings into low-dimensional spaces. More specifically, we consider the Mahalanobis distance measure, the Bhattacharyya class of divergences and the Kullback
Change Detection for Hyperspectral Sensing in a Transformed Low-dimensional Space
, 2010
"... Change detection in hyperspectral imagery is the process of comparing two spectral images of the same scene acquired at different times, and finding a small set of pixels that has the largest apparent spectral change. We present an approach that operates in a two-dimensional space rather than in the ..."
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Cited by 1 (0 self)
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Change detection in hyperspectral imagery is the process of comparing two spectral images of the same scene acquired at different times, and finding a small set of pixels that has the largest apparent spectral change. We present an approach that operates in a two-dimensional space rather than
Results 1 - 10
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80,140
