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Algorithms for non-negative matrix factorization

D Seung, +1 more
- Vol. 13, pp 556-562
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The article was published on 2001-01-01 and is currently open access. It has received 5015 citations till now. The article focuses on the topics: Non-negative matrix factorization.

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What is the Dimension of Your Binary Data

TL;DR: In this paper, the concept of normalized fractal dimension is introduced to measure the degree of dependency structure of the data and the properties of such a dimension are discussed and compared with PCA.
Proceedings ArticleDOI

Listwise Collaborative Filtering

TL;DR: This paper proposes a new ranking-oriented CF algorithm, called ListCF, which directly predicts a total order of items for each user based on similar users' probability distributions over permutations of the items, and thus differs from previous ranking- oriented memory-based CF algorithms that focus on predicting the pairwise preferences between items.
Journal ArticleDOI

Cyberphysical System With Virtual Reality for Intelligent Motion Recognition and Training

TL;DR: This paper proposes to build a comprehensive cyberphysical system (CPS) with virtual reality (VR) and intelligent sensors for motion recognition and training and uses both wearable wireless sensors and nonintrusive wireless sensors to monitor the motion training status.
Journal ArticleDOI

An Exemplar-Based Approach to Frequency Warping for Voice Conversion

TL;DR: A new voice conversion framework is presented in which frequency warping and exemplar-based method for voice conversion is combined and a significant improvement in speech quality is observed over the state-of-the-art parametric methods.
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Efficient Nonnegative Matrix Factorization via projected Newton method

TL;DR: This paper proposes a fast NMF algorithm via Projected Newton Method (PNM) to efficiently solve a nonnegative least squares problem, which achieves a quadratic convergence rate under appropriate assumptions.
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