Random Projection, Margins, Kernels, and Feature-Selection

Blum, Avrim

doi:10.1007/11752790_3

Cited by 79 publications

(97 citation statements)

References 22 publications

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“…3.4.3.3 Perceptrons using random feature projection embeddings A third machine learning approach applied during research was the Perceptron (Rosenblatt and 1958) algorithm and embeddings using random feature projection (RFP) (Blum and 2005). When dealing with data at a large scale, such as the universe of patents, sometimes it's preferable to sacrifice a small amount of accuracy for large increases in efficiency.…”

Section: Machine Learning Methodologiesmentioning

confidence: 99%

Automated patent landscaping

Abood

Feltenberger

2018

Artif Intell Law

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Patent landscaping is the process of finding patents related to a particular topic. It is important for companies, investors, governments, and academics seeking to gauge innovation and assess risk. However, there is no broadly recognized best approach to landscaping. Frequently, patent landscaping is a bespoke human-driven process that relies heavily on complex queries over bibliographic patent databases. In this paper, we present Automated Patent Landscaping, an approach that jointly leverages human domain expertise, heuristics based on patent metadata, and machine learning to generate high-quality patent landscapes with minimal effort. In particular, this paper describes a flexible automated methodology to construct a patent landscape for a topic based on an initial seed set of patents. This approach takes human-selected seed patents that are representative of a topic, such as operating systems, and uses structure inherent in patent data such as references and class codes to ''expand'' the seed set to a set of ''probably-related'' patents and anti-seed ''probably-unrelated'' patents. The expanded set of patents is then pruned with a semi-supervised machine learning model trained on seed and anti-seed patents. This removes patents from the expanded set that are unrelated to the topic and ensures a comprehensive and accurate landscape.

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Section: Machine Learning Methodologiesmentioning

confidence: 99%

Automated patent landscaping

Abood

Feltenberger

2018

Artif Intell Law

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“…We first present the following Lemma that relates the JL-Lemma to the margin of the linear hyperplane in supervised learning settings [18].…”

Section: Kernelised Orthonormal Random Projection (Korp)mentioning

confidence: 99%

“…iid from this distribution, with probability ≥ 1 − δ, there exists a vector w in span (z 1 , · · · , z d ) that has error at most ε at margin λ 2 [18].…”

Section: Kernelised Orthonormal Random Projection (Korp)mentioning

confidence: 99%

“…This is because the RKHS is essentially an infinite-dimensional Euclidean space [18]. Given a set of points which are linearly separable with margin λ under a particular kernel function, we draw d random examples , if x 1 , · · · , x d , are drawn from the same distribution, the mapping:…”

Section: Kernelised Orthonormal Random Projection (Korp)mentioning

confidence: 99%

“…However, as it is possible that some hyperplanes are not linearly independent, then the hyperplanes could be highly correlated. To that end, one needs to orthogonalise the hyperplane set S [18]. In this work, we apply QR decomposition [157] to construct a set of orthonormal basis from the original basis spanning the same subspace.…”

Section: Kernelised Orthonormal Random Projection (Korp)mentioning

confidence: 99%

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Fast and accurate image and video analysis on Riemannian manifolds

Zhao¹

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Recent times have seen increasing attention on representing images and videos on Riemannian manifolds. Such representations offer new means of preserving intrinsic data structures, which Euclidean-based representations often fail to capture. Preserving the intrinsic structure can bring superior benefits in terms of richer representations and robustness to variations. However, to intrinsically operate on the manifold incurs expensive computation complexity because of using non-linear operators. Furthermore, it is difficult to extend the existing computer vision techniques which were originally developed in Euclidean space into the manifold. To address these issues, recent research often uses extrinsic approaches such as tangent space projection and kernelised approaches. Unfortunately, these methods are not free of drawbacks in terms of low accuracy and high computational load. To that end, this research studies approaches for analysing manifold features which achieve superior performance from the manifold structures whilst computational complexity can be massively reduced. This thesis illustrates the general steps of manifold approaches for image and video analysis, here called manifold scheme, followed by discussions on the possible ways to reduce the computational complexities. Guided by the manifold scheme, this research further proposes two frameworks to analyse manifold features: random projection on Riemannian manifolds and convex hull on Symmetric Positive Definite manifolds. To solve the problems posed by each framework, we present three solutions. Through experiments on several computer vision tasks, we verified that our proposed methods can massively reduce the computational load, while still achieving competitive performance. In addition, our manifold scheme shows another possible way to reduce the computational load of manifold approaches through the generating manifold model using effective lower-level features with low dimensionality. Thus, this thesis proposes a landmark manifold model generated by effective landmark points for facial emotion recognition, which shows competitive performance with much lower computational complexity compared to state-of-the-art manifold methods. Design of experiments (10%)

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Microphone Forensics

Kraetzer¹,

Dittmann²

2015

Handbook of Digital Forensics of Multimedia Data and Devices

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Random Projection, Margins, Kernels, and Feature-Selection

Cited by 79 publications

References 22 publications

Automated patent landscaping

Automated patent landscaping

Fast and accurate image and video analysis on Riemannian manifolds

Microphone Forensics

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