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We are analyzing https://www.nature.com/articles/44565.

Title:
Learning the parts of objects by non-negative matrix factorization | Nature
Description:
Is perception of the whole based on perception of its parts? There is psychological1 and physiological2,3 evidence for parts-based representations in the brain, and certain computational theories of object recognition rely on such representations4,5. But little is known about how brains or computers might learn the parts of objects. Here we demonstrate an algorithm for non-negative matrix factorization that is able to learn parts of faces and semantic features of text. This is in contrast to other methods, such as principal components analysis and vector quantization, that learn holistic, not parts-based, representations. Non-negative matrix factorization is distinguished from the other methods by its use of non-negativity constraints. These constraints lead to a parts-based representation because they allow only additive, not subtractive, combinations. When non-negative matrix factorization is implemented as a neural network, parts-based representations emerge by virtue of two properties: the firing rates of neurons are never negative and synaptic strengths do not change sign.
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30 years and 10 months (reg. 1994-08-11).

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Education
Technology & Computing
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Keywords {🔍}

article, google, scholar, nature, access, neural, content, cas, pubmed, parts, nonnegative, representations, cookies, analysis, data, matrix, factorization, recognition, ads, privacy, information, learn, learning, objects, seung, based, partsbased, object, algorithm, open, press, math, scientific, advertising, subscribe, lee, sebastian, brain, methods, institution, online, buy, language, networks, psychol, cortex, visual, rev, theory, cambridge,

Topics {✒️}

nature portfolio permissions reprints privacy policy author information authors individual-specific template-based representations advertising natural language processing social media nature 381 nature 401 nature grip-load force couplings bayesian-based iterative method parts-based representations emerge springerlink instant access permissions personal data negative factor analysis data protection incomplete data unsupervised neural networks principal components analysis negative matrix factorization neural network parts-based representations privacy independent component representations infant crawling based find parts bartlett adaptive algorithm based object recognition rely european economic area institutional subscriptions read single units high-level vision grolier encyclopedia corpus abnormal interlimb coordination motor developmental delay hybrid-sindy michael accepting optional cookies visual object recognition perceiving visual surfaces explore content subscription content journals search log form cognitive maps human image understanding em-type algorithms parts-based representation �wake-sleep” algorithm

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Is perception of the whole based on perception of its parts?
What is the goal of sensory coding?

Schema {🗺️}

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