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

Puolamäki, Kai; Oikarinen, Emilia; Kang, Bo; Lijffijt, Jefrey; Bie, Tijl De

doi:10.1007/s10618-019-00655-x

Cited by 9 publications

(10 citation statements)

References 35 publications

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“…The second group of interactive DR methods adjust the algorithms according to a users' inputs. SICA (Kang et al 2016) and SIDE (Puolamäki et al 2018) explicitly model the user's belief state and find linear projections that contrast to it. These two methods are linear DR methods thus cannot present non-linear structures in the low-dimensional representations.…”

Section: Related Workmentioning

confidence: 99%

Conditional t-SNE: more informative t-SNE embeddings

et al. 2020

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Dimensionality reduction and manifold learning methods such as t-distributed stochastic neighbor embedding (t-SNE) are frequently used to map high-dimensional data into a two-dimensional space to visualize and explore that data. Going beyond the specifics of t-SNE, there are two substantial limitations of any such approach: (1) not all information can be captured in a single two-dimensional embedding, and (2) to well-informed users, the salient structure of such an embedding is often already known, preventing that any real new insights can be obtained. Currently, it is not known how to extract the remaining information in a similarly effective manner. We introduce conditional t-SNE (ct-SNE), a generalization of t-SNE that discounts prior information in the form of labels. This enables obtaining more informative and more relevant embeddings. To achieve this, we propose a conditioned version of the t-SNE objective, obtaining an elegant method with a single integrated objective. We show how to efficiently optimize the objective and study the effects of the extra parameter that ct-SNE has over t-SNE. Qualitative and quantitative empirical results on synthetic and real data show ct-SNE is scalable, effective, and achieves its goal: it allows complementary structure to be captured in the embedding and provided new insights into real data.

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Section: Related Workmentioning

confidence: 99%

Conditional t-SNE: more informative t-SNE embeddings

et al. 2020

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“…The time-consuming operations are executed only by a direct command of the user, which makes the system responsive and predictable. For further details of SIDER, see [2]. Our focus in the experimental part is to show how SIDER is able to provide the user with insightful projections of data and reveal the differences between the background distribution and the data.…”

Section: Implementation and Experimentsmentioning

confidence: 99%

“…-A free open source application demonstrating the method. This paper is a summary of a tech report [2]. Related work and technical details are discussed in detail in [2].…”

Section: Introductionmentioning

confidence: 99%

Interactive Visual Data Exploration with Subjective Feedback: An Information-Theoretic Approach

Puolamäki

Oikarinen

Kang

et al. 2018

2018 IEEE 34th International Conference on Data Engineering (ICDE)

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The exploration of high-dimensional real-valued data is one of the fundamental exploratory data analysis (EDA) tasks. Existing methods use predefined criteria for the representation of data. There is a lack of methods eliciting the user's knowledge from the data and showing patterns the user does not know yet. We provide a theoretical model where the user can input the patterns she has learned as knowledge. The background knowledge is used to find a MaxEnt distribution of the data, and the user is shown maximally informative projections in which the MaxEnt distribution and the data differ the most. We provide an interactive open source EDA system, study its performance, and present use cases on real data.

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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

Cited by 9 publications

References 35 publications

Conditional t-SNE: more informative t-SNE embeddings

Conditional t-SNE: more informative t-SNE embeddings

Interactive Visual Data Exploration with Subjective Feedback: An Information-Theoretic Approach

Supervised Human-Guided Data Exploration

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