PATTERN CATEGORY ASSIGNMENT BY NEURAL NETWORKS AND NEAREST NEIGHBORS RULE: A SYNOPSIS AND A CHARACTERIZATION

1996 ◽  
Vol 10 (05) ◽  
pp. 393-408 ◽  
Author(s):  
A. MITICHE ◽  
J. K. AGGARWAL
Keyword(s):  
Neural Networks ◽  
Character Recognition ◽  
Optical Character Recognition ◽  
Nearest Neighbor ◽  
Nearest Neighbors ◽  
Automatic Target Recognition ◽  
Nearest Neighbor Search ◽  
Neighbor Search ◽  
Category Assignment

The purpose of this paper is two-fold: to give a synoptic description of favored neural networks and to characterize the potency of these neural networks as pattern classifiers, against the background of the familiar nearest neighbors classification. We limit the study to those neural network structures most commonly used for pattern classification: the multilayer perceptron, the Kohonen associative memory, and the Carpenter–Grossberg clustering network, for which we give a tutorial description with the aim of making the driving concepts apparent. The nearest neighbors rule is presented with improved nearest neighbor search and reference data sample pruning. To gain some familiarity with the classifiers, we expound the sequence of computations implicated in pattern category assignment by each classifier. A characterization of the classifiers is drawn from observed and expected properties and from experiments in automatic target recognition and optical character recognition as summarized in comparative tables of performance. This characterization supports the suggestion that nearest neighbors classification always be considered before endorsing alternative pattern classifiers such as neural networks.

scholarly journals Learning Low-Dimensional Embeddings of Audio Shingles for Cross-Version Retrieval of Classical Music

2019 ◽  
Vol 10 (1) ◽  
pp. 19 ◽  
Author(s):  
Frank Zalkow ◽  
Meinard Müller
Keyword(s):  
Neural Networks ◽  
Dimensionality Reduction ◽  
Classical Music ◽  
Nearest Neighbor ◽  
Nearest Neighbor Search ◽  
Neighbor Search ◽  
Reduction Methods ◽  
Retrieval Problem ◽  
Low Dimensional ◽  
Western Classical Music

Cross-version music retrieval aims at identifying all versions of a given piece of music using a short query audio fragment. One previous approach, which is particularly suited for Western classical music, is based on a nearest neighbor search using short sequences of chroma features, also referred to as audio shingles. From the viewpoint of efficiency, indexing and dimensionality reduction are important aspects. In this paper, we extend previous work by adapting two embedding techniques; one is based on classical principle component analysis, and the other is based on neural networks with triplet loss. Furthermore, we report on systematically conducted experiments with Western classical music recordings and discuss the trade-off between retrieval quality and embedding dimensionality. As one main result, we show that, using neural networks, one can reduce the audio shingles from 240 to fewer than 8 dimensions with only a moderate loss in retrieval accuracy. In addition, we present extended experiments with databases of different sizes and different query lengths to test the scalability and generalizability of the dimensionality reduction methods. We also provide a more detailed view into the retrieval problem by analyzing the distances that appear in the nearest neighbor search.

The Earth Mover’s Distance as a Metric for the Space of Inorganic Compositions

2020 ◽  
Author(s):  
Cameron Hargreaves ◽  
Matthew Dyer ◽  
Michael Gaultois ◽  
Vitaliy Kurlin ◽  
Matthew J Rosseinsky
Keyword(s):  
Euclidean Distance ◽  
Nearest Neighbor ◽  
Nearest Neighbor Search ◽  
Inorganic Crystal Structure Database ◽  
Earth Mover’S Distance ◽  
Chemical Similarity ◽  
Earth Mover's Distance ◽  
Neighbor Search ◽  
The Earth ◽  
Binary Compounds

It is a core problem in any field to reliably tell how close two objects are to being the same, and once this relation has been established we can use this information to precisely quantify potential relationships, both analytically and with machine learning (ML). For inorganic solids, the chemical composition is a fundamental descriptor, which can be represented by assigning the ratio of each element in the material to a vector. These vectors are a convenient mathematical data structure for measuring similarity, but unfortunately, the standard metric (the Euclidean distance) gives little to no variance in the resultant distances between chemically dissimilar compositions. We present the Earth Mover’s Distance (EMD) for inorganic compositions, a well-defined metric which enables the measure of chemical similarity in an explainable fashion. We compute the EMD between two compositions from the ratio of each of the elements and the absolute distance between the elements on the modified Pettifor scale. This simple metric shows clear strength at distinguishing compounds and is efficient to compute in practice. The resultant distances have greater alignment with chemical understanding than the Euclidean distance, which is demonstrated on the binary compositions of the Inorganic Crystal Structure Database (ICSD). The EMD is a reliable numeric measure of chemical similarity that can be incorporated into automated workflows for a range of ML techniques. We have found that with no supervision the use of this metric gives a distinct partitioning of binary compounds into clear trends and families of chemical property, with future applications for nearest neighbor search queries in chemical database retrieval systems and supervised ML techniques.

scholarly journals PCT: Point cloud transformer

2021 ◽  
Vol 7 (2) ◽  
pp. 187-199
Author(s):  
Meng-Hao Guo ◽  
Jun-Xiong Cai ◽  
Zheng-Ning Liu ◽  
Tai-Jiang Mu ◽  
Ralph R. Martin ◽  
...  
Keyword(s):  
Language Processing ◽  
Point Cloud ◽  
Nearest Neighbor ◽  
Semantic Segmentation ◽  
Nearest Neighbor Search ◽  
Local Context ◽  
Irregular Domain ◽  
Cloud Processing ◽  
Neighbor Search ◽  
Farthest Point

AbstractThe irregular domain and lack of ordering make it challenging to design deep neural networks for point cloud processing. This paper presents a novel framework named Point Cloud Transformer (PCT) for point cloud learning. PCT is based on Transformer, which achieves huge success in natural language processing and displays great potential in image processing. It is inherently permutation invariant for processing a sequence of points, making it well-suited for point cloud learning. To better capture local context within the point cloud, we enhance input embedding with the support of farthest point sampling and nearest neighbor search. Extensive experiments demonstrate that the PCT achieves the state-of-the-art performance on shape classification, part segmentation, semantic segmentation, and normal estimation tasks.

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