SICA: subjectively interesting component analysis

Kang, Bo; Lijffijt, Jefrey; Santos-Rodríguez, Raúl; Bie, Tijl De

doi:10.1007/s10618-018-0558-x

Cited by 4 publications

(5 citation statements)

References 27 publications

(20 reference statements)

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“…While this was sufficient to find previously unknown behaviors in models explored in this work, the actual priors modelers have while studying models might be different than our chosen heuristic. Similar to the work of De Bie [25], we would want users of Hint to be able to flexibly define priors that fit their beliefs, such that the highlighted samples will be as effective in updating said priors as possible. We believe that such automated tools to explore computational models can serve as a vehicle to accelerate scientific discoveries in many fields.…”

Section: Discussionmentioning

confidence: 99%

“…By defining learning as the compression of models, Schmidhuber [38] defined interestingness as the derivative of model compression introduced by observing a new sample. Similarly, the subjective interestingness theory [25] defines interestingness as the update in users' beliefs after observing the data. In his work on SICA [25], De Bie proposes a rigorous mathematical framework for reducing the dimensionality of data such that it will organize datasets with respect to their update of users' priors.…”

Section: Related Workmentioning

confidence: 99%

“…Similarly, the subjective interestingness theory [25] defines interestingness as the update in users' beliefs after observing the data. In his work on SICA [25], De Bie proposes a rigorous mathematical framework for reducing the dimensionality of data such that it will organize datasets with respect to their update of users' priors.…”

Section: Related Workmentioning

confidence: 99%

“…This automation of exploration requires a theoretical basis to rigorously define what is interesting to explore. The works of [25,38] suggest that an observed sample is interesting if the observers update their understanding of the system after viewing the sample.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Discovering Unexpected Local Nonlinear Interactions in Scientific Black-box Models

Doron

Segev

Shahaf

2019

Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery &Amp; Data Mining

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Scientific computational models are crucial for analyzing and understanding complex real-life systems that are otherwise difficult for experimentation. However, the complex behavior and the vast inputoutput space of these models often make them opaque, slowing the discovery of novel phenomena. In this work, we present Hint (Hessian INTerestingness)-a new algorithm that can automatically and systematically explore black-box models and highlight local nonlinear interactions in the input-output space of the model. This tool aims to facilitate the discovery of interesting model behaviors that are unknown to the researchers. Using this simple yet powerful tool, we were able to correctly rank all pairwise interactions in known benchmark models and do so faster and with greater accuracy than state-of-the-art methods. We further applied Hint to existing computational neuroscience models, and were able to reproduce important scientific discoveries that were published years after the creation of those models. Finally, we ran Hint on two real-world models (in neuroscience and earth science) and found new behaviors of the model that were of value to domain experts.

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Section: Discussionmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Discovering Unexpected Local Nonlinear Interactions in Scientific Black-box Models

Doron

Segev

Shahaf

2019

Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery &Amp; Data Mining

View full text Add to dashboard Cite

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“…In both of these works the users can encode their knowledge as constraints. Later, these ideas have been realised as parts of working EDA systems with DR methods able to show the user what the user does not already know and able to absorb the relations the user has learned from the data, see, e.g., [5,12,13,18,19,21,25]. The drawback in all of these works is, however, that the EDA process is unguided: the user is shown something she or he does not know and what is therefore by definition always a surprise.…”

Section: Introduction and Related Workmentioning

confidence: 99%

Supervised Human-Guided Data Exploration

Oikarinen

Puolamäki

Khoshrou

et al. 2020

Machine Learning and Knowledge Discovery in Databases

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An exploratory data analysis system should be aware of what a user already knows and what the user wants to know of the data. Otherwise it is impossible to provide the user with truly informative and useful views of the data. In our recently introduced framework for human-guided data exploration (Puolamäki et al. [20]), both the user's knowledge and objectives are modelled as distributions over data, parametrised by tile constraints. This makes it possible to show the users the most informative views given their current knowledge and objectives. Often the data, however, comes with a class label and the user is interested only of the features informative related to the class. In noninteractive settings there exist dimensionality reduction methods, such as supervised PCA (Barshan et al. [1]), to make such visualisations, but no such method takes the user's knowledge or objectives into account. Here, we formulate an information criterion for supervised human-guided data exploration to find the most informative views about the class structure of the data by taking both the user's current knowledge and objectives into account. We study experimentally the scalability of our method for interactive use, and stability with respect to the size of the class of interest. We show that our method gives understandable and useful results when analysing real-world datasets, and a comparison to SPCA demonstrates the effect of the user's background knowledge. The implementation will be released as an open source software library.

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Interactive visual data exploration with subjective feedback: an information-theoretic approach

Puolamäki

Oikarinen

Kang

et al. 2019

Data Min Knowl Disc

Self Cite

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Visual exploration of high-dimensional real-valued datasets is a fundamental task in exploratory data analysis (EDA). Existing methods use predefined criteria to choose the representation of data. There is a lack of methods that (i) elicit from the user what she has learned from the data and (ii) show patterns that she does not know yet. We construct a theoretical model where identified patterns can be input as knowledge to the system. The knowledge syntax here is intuitive, such as "this set of points forms a cluster", and requires no knowledge of maths. This background knowledge is used to find a Maximum Entropy distribution of the data, after which the system provides the user data projections in which the data and the Maximum Entropy distribution differ the most, hence showing the user aspects of the data that are maximally informative given the user's current knowledge. We provide an open source EDA system with tailored interactive visualizations to demonstrate these concepts. We study the performance of the system and present use cases on both synthetic and real data. We find that the model and the prototype system allow the user to learn information efficiently from various data sources and the system works sufficiently fast in practice. We conclude that the information theoretic approach to exploratory data analysis where patterns observed by a user are formalized as constraints provides a principled, intuitive, and efficient basis for constructing an EDA system.

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SICA: subjectively interesting component analysis

Cited by 4 publications

References 27 publications

Discovering Unexpected Local Nonlinear Interactions in Scientific Black-box Models

Discovering Unexpected Local Nonlinear Interactions in Scientific Black-box Models

Supervised Human-Guided Data Exploration

Interactive visual data exploration with subjective feedback: an information-theoretic approach

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