658 research outputs found
Non-negative mixtures
This is the author's accepted pre-print of the article, first published as M. D. Plumbley, A. Cichocki and R. Bro. Non-negative mixtures. In P. Comon and C. Jutten (Ed), Handbook of Blind Source Separation: Independent Component Analysis and Applications. Chapter 13, pp. 515-547. Academic Press, Feb 2010. ISBN 978-0-12-374726-6 DOI: 10.1016/B978-0-12-374726-6.00018-7file: Proof:p\PlumbleyCichockiBro10-non-negative.pdf:PDF owner: markp timestamp: 2011.04.26file: Proof:p\PlumbleyCichockiBro10-non-negative.pdf:PDF owner: markp timestamp: 2011.04.2
Deep clustering: Discriminative embeddings for segmentation and separation
We address the problem of acoustic source separation in a deep learning
framework we call "deep clustering." Rather than directly estimating signals or
masking functions, we train a deep network to produce spectrogram embeddings
that are discriminative for partition labels given in training data. Previous
deep network approaches provide great advantages in terms of learning power and
speed, but previously it has been unclear how to use them to separate signals
in a class-independent way. In contrast, spectral clustering approaches are
flexible with respect to the classes and number of items to be segmented, but
it has been unclear how to leverage the learning power and speed of deep
networks. To obtain the best of both worlds, we use an objective function that
to train embeddings that yield a low-rank approximation to an ideal pairwise
affinity matrix, in a class-independent way. This avoids the high cost of
spectral factorization and instead produces compact clusters that are amenable
to simple clustering methods. The segmentations are therefore implicitly
encoded in the embeddings, and can be "decoded" by clustering. Preliminary
experiments show that the proposed method can separate speech: when trained on
spectrogram features containing mixtures of two speakers, and tested on
mixtures of a held-out set of speakers, it can infer masking functions that
improve signal quality by around 6dB. We show that the model can generalize to
three-speaker mixtures despite training only on two-speaker mixtures. The
framework can be used without class labels, and therefore has the potential to
be trained on a diverse set of sound types, and to generalize to novel sources.
We hope that future work will lead to segmentation of arbitrary sounds, with
extensions to microphone array methods as well as image segmentation and other
domains.Comment: Originally submitted on June 5, 201
Radical Recognition in Off-Line Handwritten Chinese Characters Using Non-Negative Matrix Factorization
In the past decade, handwritten Chinese character recognition has received renewed interest with the emergence of touch screen devices. Other popular applications include on-line Chinese character dictionary look-up and visual translation in mobile phone applications. Due to the complex structure of Chinese characters, this classification task is not exactly an easy one, as it involves knowledge from mathematics, computer science, and linguistics.
Given a large image database of handwritten character data, the goal of my senior project is to use Non-Negative Matrix Factorization (NMF), a recent method for finding a suitable representation (parts-based representation) of image data, to detect specific sub-components in Chinese characters. NMF has only been applied to typed (printed) Chinese characters in different fonts. This project focuses specifically on how well NMF works on handwritten characters. In addition, research in Chinese character classification has mainly been done using holistic approaches - treating each character as an inseparable unit. By using NMF, this project takes a different approach by focusing on a more specific problem in Chinese character classification: radical (sub-component) detection.
Finally, a possible application of radical detection will be proposed. This interactive application can potentially help Chinese language learners better recognize characters by radicals
Nonnegative Matrix Factorization: Theory with an application to translations invariant image processing
Nonnegative Matrix Factorization(NMF) is a common used technique in machine learning to extract features out of data such as text documents and images thanks to its natural clustering properties and the easy interpretation of the output data.
We review the original NMF problem, its common variants, and the main solving algorithms used nowadays. We'll also see how its particular framework makes it suitable for a lot of applications like clustering and text mining.
One of the main applications of NMF is the analysis and decomposition of images, but it can't recognize the objects if they're located in different places on multiple images, so the input data must always be pre-calibrated and adjusted. We present a way to fix this problem, that keeps the interpretability property of the output to represent the wanted parts of images, doesn't change the original input data, and bounds the computational cost by the number of effective features we want to find. We'll describe a new domain for the variables in the matrices, and we devise a method to solve the new problem, with experiments on handmade data
Group invariance principles for causal generative models
The postulate of independence of cause and mechanism (ICM) has recently led
to several new causal discovery algorithms. The interpretation of independence
and the way it is utilized, however, varies across these methods. Our aim in
this paper is to propose a group theoretic framework for ICM to unify and
generalize these approaches. In our setting, the cause-mechanism relationship
is assessed by comparing it against a null hypothesis through the application
of random generic group transformations. We show that the group theoretic view
provides a very general tool to study the structure of data generating
mechanisms with direct applications to machine learning.Comment: 16 pages, 6 figure
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