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5,229
Data cube: A relational aggregation operator generalizing group-by, cross-tab, and sub-totals
, 1996
"... Abstract. Data analysis applications typically aggregate data across many dimensions looking for anomalies or unusual patterns. The SQL aggregate functions and the GROUP BY operator produce zero-dimensional or one-dimensional aggregates. Applications need the N-dimensional generalization of these op ..."
Abstract
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Cited by 860 (11 self)
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Abstract. Data analysis applications typically aggregate data across many dimensions looking for anomalies or unusual patterns. The SQL aggregate functions and the GROUP BY operator produce zero-dimensional or one-dimensional aggregates. Applications need the N-dimensional generalization
Training Linear SVMs in Linear Time
, 2006
"... Linear Support Vector Machines (SVMs) have become one of the most prominent machine learning techniques for high-dimensional sparse data commonly encountered in applications like text classification, word-sense disambiguation, and drug design. These applications involve a large number of examples n ..."
Abstract
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Cited by 549 (6 self)
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Linear Support Vector Machines (SVMs) have become one of the most prominent machine learning techniques for high-dimensional sparse data commonly encountered in applications like text classification, word-sense disambiguation, and drug design. These applications involve a large number of examples n
Edge Detection and Ridge Detection with Automatic Scale Selection
- CVPR'96
, 1996
"... When extracting features from image data, the type of information that can be extracted may be strongly dependent on the scales at which the feature detectors are applied. This article presents a systematic methodology for addressing this problem. A mechanism is presented for automatic selection of ..."
Abstract
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Cited by 347 (24 self)
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of scale levels when detecting one-dimensional features, such as edges and ridges. Anovel concept of a scale-space edge is introduced, defined as a connected set of points in scale-space at which: (i) the gradient magnitude assumes a local maximum in the gradient direction, and (ii) a normalized measure
The SR-tree: An Index Structure for High-Dimensional Nearest Neighbor Queries
, 1997
"... Recently, similarity queries on feature vectors have been widely used to perform content-based retrieval of images. To apply this technique to large databases, it is required to develop multidimensional index structures supporting nearest neighbor queries e ciently. The SS-tree had been proposed for ..."
Abstract
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Cited by 438 (3 self)
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for this purpose and is known to outperform other index structures such as the R*-tree and the K-D-B-tree. One of its most important features is that it employs bounding spheres rather than bounding rectangles for the shape of regions. However, we demonstrate in this paper that bounding spheres occupy much larger
Cubic convolution interpolation for digital image processing
- IEEE Trans. Acoust., Speech, Signal Process
, 1981
"... Absfrucf-Cubic convolution interpolation is a new technique for resampling discrete data. It has a number of desirable features which make it useful for image processing. The technique can be performed efficiently on a digital computer. The cubic convolution interpolation function converges uniforml ..."
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Cited by 370 (0 self)
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splines. A one-dimensional interpolation function is derived in this paper. A separable extension of this algorithm to two dimensions is applied to image data. I
Blobworld: A System for Region-Based Image Indexing and Retrieval
- In Third International Conference on Visual Information Systems
, 1999
"... . Blobworld is a system for image retrieval based on finding coherent image regions which roughly correspond to objects. Each image is automatically segmented into regions ("blobs") with associated color and texture descriptors. Querying is based on the attributes of one or two regions of ..."
Abstract
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Cited by 375 (4 self)
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of interest, rather than a description of the entire image. In order to make large-scale retrieval feasible, we index the blob descriptions using a tree. Because indexing in the high-dimensional feature space is computationally prohibitive, we use a lower-rank approximation to the high-dimensional distance
The Turn Model for Adaptive Routing
- JOURNAL OF ACM
, 1994
"... This paper presents a model for designing wormhole routing algorithms, A unique feature of the model is th~t lt is not based cm adding physical or virtual channels to direct networks (although it can be applied to networks with extra channels). Instead, the model is based [In analyzlng the directio ..."
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Cited by 361 (6 self)
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This paper presents a model for designing wormhole routing algorithms, A unique feature of the model is th~t lt is not based cm adding physical or virtual channels to direct networks (although it can be applied to networks with extra channels). Instead, the model is based [In analyzlng
Camera Calibration with One-Dimensional Objects
, 2004
"... Camera calibration has been studied extensively in computer vision and photogrammetry and the proposed techniques in the literature include those using 3D apparatus (two or three planes orthogonal to each other or a plane undergoing a pure translation, etc.), 2D objects (planar patterns undergoing ..."
Abstract
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Cited by 69 (1 self)
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unknown motions), and 0D features (self-calibration using unknown scene points). Yet, this paper proposes a new calibration technique using 1D objects (points aligned on a line), thus filling the missing dimension in calibration. In particular, we show that camera calibration is not possible with free
Support vector machines for spam categorization
- IEEE TRANSACTIONS ON NEURAL NETWORKS
, 1999
"... We study the use of support vector machines (SVM’s) in classifying e-mail as spam or nonspam by comparing it to three other classification algorithms: Ripper, Rocchio, and boosting decision trees. These four algorithms were tested on two different data sets: one data set where the number of features ..."
Abstract
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Cited by 342 (2 self)
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of features were constrained to the 1000 best features and another data set where the dimensionality was over 7000. SVM’s performed best when using binary features. For both data sets, boosting trees and SVM’s had acceptable test performance in terms of accuracy and speed. However, SVM’s had significantly
Cones of matrices and set-functions and 0-1 optimization
- SIAM JOURNAL ON OPTIMIZATION
, 1991
"... It has been recognized recently that to represent a polyhedron as the projection of a higher dimensional, but simpler, polyhedron, is a powerful tool in polyhedral combinatorics. We develop a general method to construct higher-dimensional polyhedra (or, in some cases, convex sets) whose projection a ..."
Abstract
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Cited by 347 (7 self)
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approximates the convex hull of 0-1 valued solutions of a system of linear inequalities. An important feature of these approximations is that one can optimize any linear objective function over them in polynomial time. In the special case of the vertex packing polytope, we obtain a sequence of systems
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