A generalized k-nearest neighbor rule

Patrick, Edward A.; Fischer, Frederic P.

doi:10.1016/s0019-9958(70)90081-1

Cited by 157 publications

(50 citation statements)

References 5 publications

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“…In both AGES and AGES lda , the dimension of the aging pattern subspace is set to 20 ðd ¼ 20Þ, the maximum iteration ¼ 50, and the error threshold ¼ 10 À3 . In this experiment, AGES is compared with WAS [13], AAS [12], as well as some conventional classification methods including k-Nearest Neighbors (kNN) [16], Back Propagation neural network (BP) [21], C4.5 decision tree (C4.5) [17], and Support Vector Machines (SVM) [26]. The algorithms are first tested on the FG-NET Aging Database through the Leave-One-Person-Out (LOPO) mode, i.e., in each fold, the images of one person are used as the test set and those of the others are used as the training set.…”

Section: Methodsmentioning

confidence: 99%

Automatic Age Estimation Based on Facial Aging Patterns

Geng

Zhou

Smith‐Miles

2007

IEEE Trans. Pattern Anal. Mach. Intell.

840

515

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Abstract-While recognition of most facial variations, such as identity, expression, and gender, has been extensively studied, automatic age estimation has rarely been explored. In contrast to other facial variations, aging variation presents several unique characteristics which make age estimation a challenging task. This paper proposes an automatic age estimation method named AGES (AGing pattErn Subspace). The basic idea is to model the aging pattern, which is defined as the sequence of a particular individual's face images sorted in time order, by constructing a representative subspace. The proper aging pattern for a previously unseen face image is determined by the projection in the subspace that can reconstruct the face image with minimum reconstruction error, while the position of the face image in that aging pattern will then indicate its age. In the experiments, AGES and its variants are compared with the limited existing age estimation methods (WAS and AAS) and some well-established classification methods (kNN, BP, C4.5, and SVM). Moreover, a comparison with human perception ability on age is conducted. It is interesting to note that the performance of AGES is not only significantly better than that of all the other algorithms, but also comparable to that of the human observers.

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Section: Methodsmentioning

confidence: 99%

Automatic Age Estimation Based on Facial Aging Patterns

Geng

Zhou

Smith‐Miles

2007

IEEE Trans. Pattern Anal. Mach. Intell.

840

515

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“…A nearest-neighbour algorithm [48] is used to identify bubbles and track their displacement between shortly delayed simulation time steps, Δt = 10 -2 s. Bubble data concerning volume, aspect ratio and centroid coordinates (x, y, z) are compared between consecutive transient results. The bubble identification is carried out by minimization of the global 'distance' between bubble data at the analyzed time steps under certain restrictions:…”

Section: Bubble Discriminationmentioning

confidence: 99%

Use of α -shapes for the measurement of 3D bubbles in fluidized beds from two-fluid model simulations

et al. 2016

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A geometrical technique based on shape construction was employed to reconstruct the simulated domain of 3D bubbles in a gas-solid fluidized bed, from Two-Fluid Model (TFM) simulations. The Delaunay triangulation of the cloud of points that represent volume fraction iso-surfaces in transient TFM simulations was filtered by means of the so-called α-shapes, allowing a topologically accurate description of 3D bubbles within a fluidized bed. Consequently, individual 3D bubble properties such as size and velocity were measured. Simulated bubble characteristics were further compared to those measured on pseudo-2D bed facilities by image techniques in order to illustrate the effect of the bed geometry on the bubbling behaviour under mimicked operational conditions.

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“…This formula can be easily generalized to a k-nearest neighbours rule by introducing a voting scheme for deciding a class of the investigated case [7,8,17,110,111]. A voting scheme can also be applied in a case when in U there are several objects which are equally similar to y and belong to different decision classes.…”

Section: Simmentioning

confidence: 99%

Algorithms for Similarity Relation Learning from High Dimensional Data

Janusz

2014

Transactions on Rough Sets XVII

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Abstract. The notion of similarity plays an important role in machine learning and artificial intelligence. It is widely used in tasks related to a supervised classification, clustering, an outlier detection and planning. Moreover, in domains such as information retrieval or case-based reasoning, the concept of similarity is essential as it is used at every phase of the reasoning cycle. The similarity itself, however, is a very complex concept that slips out from formal definitions. A similarity of two objects can be different depending on a considered context. In many practical situations it is difficult even to evaluate the quality of similarity assessments without considering the task for which they were performed. Due to this fact the similarity should be learnt from data, specifically for the task at hand. This paper presents a research on the problem of similarity learning, which is a part of author's PHD dissertation. It describes a similarity model, called Rule-Based Similarity, and shows algorithms for constructing this model from available data. The model utilizes notions from the rough set theory to derive a similarity function that allows to approximate the similarity relation in a given context. It is largely inspired by the idea of Tversky's feature contrast model and it has several analogical properties. In the paper, those theoretical properties are described and discussed. Moreover, the paper presents results of experiments on real-life data sets, in which a quality of the proposed model is thoroughly evaluated and compared with the state-of-the-art algorithms.

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A generalized k-nearest neighbor rule

Cited by 157 publications

References 5 publications

Automatic Age Estimation Based on Facial Aging Patterns

Automatic Age Estimation Based on Facial Aging Patterns

Use of α -shapes for the measurement of 3D bubbles in fluidized beds from two-fluid model simulations

Algorithms for Similarity Relation Learning from High Dimensional Data

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