The Information Bottleneck Method

Naftali Tishby, Fernando C. Pereira, William Bialek · Allerton Conference / arXiv · 1999 · information-theory · record tishby1999-information-bottleneck
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Other fields define these differently the same word, defined another way in another discipline
information 4 fields

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Every tag the record carries, each linking to every other paper in the corpus that carries it. A tag appears here whether or not its words occur in the article — the spans below are only the ones a sentence in this paper licenses. Of the 7 concept tags on this paper, 5 are grounded in a quoted sentence.
information bottleneck mutual information rate-distortion relevant information distortion function relevance variable meaningful information relevance is supplied by a SECOND VARIABLE with positive mutual information, in place of a chosen distortion function, and the joint distribution of the two is assumed available one Lagrange multiplier carries the whole tradeoff, and its two limits are stated: at zero everything collapses to a single codeword, and as it grows the quantization becomes arbitrarily fine the Markov condition is stated NOT to be a modelling assumption - the compressed variable is not a hidden variable in a model of the data - and the paper names the different condition a model would have required instead the compressed representation and its representatives are solved for TOGETHER, which is what separates this from rate distortion theory, where choosing representatives is a separate problem convergence of the alternating iterations is argued from each step minimising the same functional independently, that functional being bounded below as a sum of two non-negative terms raising the multiplier traces convex curves in the information plane, and pairs of curves separate at a finite critical value through a second order phase transition, producing a hierarchy of quantizations information signal distortion compression bottleneck rate relevant quantization information about Y Bayesian mean average of mathematical-proof

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claim typeclaim_typethe shape of the claim: existence, mechanism, method, critique, prediction, null
concept edgeconcept_edgesan asserted relation between two concepts, with the sentence asserting it
conceptconceptsan idea the paper works with, quoted where the paper uses it
content tagcontent_tagsa finding, definition, quantity, statistic or variable named in the paper
limitationlimitationswhat the paper says its evidence cannot support
mechanismmechanismshow the reported effect is said to come about
methodmethodhow the paper produced its evidence
phenomenonphenomenonthe effect or regularity under study
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transfertransfersa method or concept carried in from another field

The paper

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claim_relations 5 · claim_type 1 · concept_edges 1 · concepts 5 · content_tags 7 · limitations 8 · mechanisms 6 · method 1 · phenomenon 1 · senses 4 · transfers 1

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facettagfieldconf.whose words
claim_typemethod
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claim_typethe paper's words
phenomenonrelevant information extraction under lossy compression
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phenomenonthe paper's words
methodmathematical-proof
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methodthe paper's words
mechanismsmechanisms
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mechanisms[0]0.9the tagger's words
mechanismsmechanisms
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mechanisms[1]0.9the tagger's words
mechanismsmechanisms
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mechanisms[2]0.9the tagger's words
mechanismsmechanisms
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mechanisms[3]0.9the tagger's words
mechanismsthat the alternation runs over THREE convex sets is stated in Theorem 5, a different span, not in this one
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mechanisms[4]0.9the tagger's words
mechanismsmechanisms
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mechanisms[5]0.9the tagger's words
limitationsThe convergence proof does not establish that the solution is unique, since the functional is convex in each distribution separately but not jointly.
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what this limits: any claim that the method finds the solution: THUS OUR CONVERGENCE PROOF DOES NOT IMPLY UNIQUENESS OF THE SOLUTION
limitations[0]the tagger's words
limitationsThe solution is formal: the conditional distribution appearing in the exponent is defined only implicitly in terms of the mapping being solved for.
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what this limits: the solution as something to compute with: IT IS A FORMAL SOLUTION SINCE p(y|x~) IN THE EXPONENTIAL IS DEFINED IMPLICITLY IN TERMS OF THE ASSIGNMENT MAPPING
limitations[1]the tagger's words
limitationsThe structure and cardinality of the compressed representation still have to be specified externally and are not determined by the theory.
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what this limits: the claim that the method determines the representation: THE FORMAL SOLUTION ... STILL REQUIRES A SPECIFICATION OF THE STRUCTURE AND CARDINALITY OF X~, AS IN RATE DISTORTION THEORY
limitations[2]the tagger's words
limitationsThe method assumes access to the joint distribution; how to obtain an adequate sample of it is left outside the paper.
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what this limits: every application to finite data: THE PROBLEM OF ACTUALLY OBTAINING A GOOD ENOUGH SAMPLE OF THIS DISTRIBUTION IS AN INTERESTING ISSUE IN LEARNING THEORY, BUT IS BEYOND THE SCOPE OF THIS PAPER
limitations[3]the tagger's words
limitationsThe convergence proof for the bottleneck iterations is only sketched in this paper for reasons of length.
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what this limits: the convergence result as a proved theorem in this paper: FOR LACK OF SPACE WE CAN ONLY OUTLINE THE PROOF
limitations[4]the tagger's words
limitationsThe problem the paper addresses is stated to be ill-posed until someone supplies a definition of relevance, which is a choice and not a measurement.
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what this limits: any claim that a compressed representation keeps the relevant information: the problem is not well posed without a suitable definition of relevance, so the definition is supplied by the analyst before any result exists
limitations[5]1.0the tagger's words
limitationsChoosing the distortion function is choosing which features matter, and the paper says the correct one is virtually impossible to obtain for a real signal.
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what this limits: a rate-distortion result as a statement about which parts of a signal matter: the distortion function is an implicit feature selection, and the paper states it is virtually impossible to come up with the correct one for acoustic signals
limitations[6]1.0the tagger's words
limitationsThe trade-off the method exposes has no uniquely right setting; the balance point is a parameter the analyst turns.
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what this limits: any single information-bottleneck solution: compression is traded against preserved meaningful information, there is no single right solution for that trade-off, and the balance is set by the Lagrange multiplier
limitations[7]1.0the tagger's words
transfersthe rate-distortion trade-off curve and its cardinality specification, taken from lossy source coding and re-drawn in the information plane as the bottleneck curve
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transfers[0]0.85the tagger's words
conceptsmutual information
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concepts[1]the paper's words
conceptsrelevant information
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concepts[4]the paper's words
conceptsdistortion function
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concepts[5]the paper's words
conceptsrelevance variable
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concepts[6]the paper's words
conceptsmeaningful information
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concepts[7]the paper's words
sensesrelevant information - DEFINED BY A CHOSEN SECOND VARIABLE: what a signal tells you about another signal Y, so relevance is never a property of the signal alone and the choice of Y fixes which features count
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senses[0]1.0the tagger's words
sensesrelevance - AS SET BY THE ANALYST'S PURPOSE: the same speech signal has different relevant features depending on whether the task is recognition or speaker identification, and the paper lists the choice per domain
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senses[1]1.0the tagger's words
sensesmeaning - WHAT SHANNON DELIBERATELY LEFT OUT: information theory was formulated about transmitting information rather than judging its value to the recipient, and this paper returns to the excluded question
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senses[2]1.0the tagger's words
sensesthe distortion measure - EMERGENT, NOT ASSUMED: the KL divergence falls out of the variational principle rather than being chosen in advance, which is the paper's answer to the arbitrariness it identifies
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senses[3]1.0the tagger's words
claim_relationsextends → rate-distortion theory (Shannon)
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claim_relations[0]the tagger's words
claim_relationscontradicts → the arbitrary choice of distortion function in rate-distortion theory
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claim_relations[1]the tagger's words
claim_relationsextends → rate distortion theory
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claim_relations[2]the tagger's words
claim_relationsextends → the Blahut–Arimoto algorithm
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claim_relations[3]the tagger's words
claim_relationsbuilds-on → the convergence lemma of Csiszár and Tusnády
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claim_relations[4]the tagger's words
concept_edgesrelevant information —operationalised-as→ distortion function
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strength of the relation: 1 Audit 2026-09-28: DOUBTFUL - read against its full span and kept, but the span says the distortion function IMPLICITLY specifies the relevant aspects; operationalised-as is loose.
concept_edges[0]the tagger's words
content_tagsThe information X gives about Y is squeezed through a bottleneck of codewords, a constrained optimization that generalizes rate-distortion theory with the distortion measure emerging from the joint statistics of X and Y.
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content_tags.findings[0]the tagger's words
content_tagsThe method yields an exact set of self-consistent equations solvable by a convergent re-estimation generalizing the Blahut–Arimoto algorithm.
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content_tags.findings[1]the tagger's words
content_tagsinformation about Y
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content_tags.variables[1]the tagger's words
content_tagsrelevant information
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content_tags.definitions[0]the tagger's words
content_tagsinformation bottleneck
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content_tags.definitions[1]the tagger's words
content_tagsBayesian
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also in: Bayesian
content_tags.statistics[0]the tagger's words
content_tagsmean
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also in: mean
content_tags.statistics[1]the tagger's words

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