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Report

Teaser, summary, work performed and final results

Periodic Reporting for period 2 - FACTORY (New paradigms for latent factor estimation)

Teaser

Summary

\"Our research is about machine learning & signal processing, which are subareas of artificial intelligence & data science. It concerns the analysis & processing of data that is organised into a tabular or \"\"matrix\"\". For example, the columns of the matrix may describe users of a music streaming service and the rows may represent songs of the catalog. Each entry or coefficient of the matrix may contain the number of times a given user listened to a specific song, or alternatively the rating s/he gave to that song. A second example would be a matrix whose entries contain the number of occurrences of specific words (indexed by rows) in a corpus of text documents (indexed by columns). A third example would be a spectrogram, i.e., a matrix whose entries represent the spectral content (with frequencies indexed by rows) of a musical signal along time (indexed by columns).

Analysing & processing such data often entails unveiling latent structures in the forms of \"\"patterns\"\" that explain the data. In the above examples, these patterns may tell about 1) the musical preference of subgroups of users, 2) topics addressed in specific documents, 3) individual notes or chords played in the music signal. These patterns can be discovered by computing an approximate decomposition of the matrix data into the product of two smaller matrices, so-called \"\"factors\"\". The first factor yields the recurring patterns characteristic of the data. The second factor describes in which proportions each data sample is made of these patterns. Computing this decomposition (or \"\"factorisation\"\") involves a mathematical procedure called optimisation. It consists of designing an algorithm whose objective is to minimise a numerical measure of fit between the collected data and its factorised approximation.

Our research consists of exploring new paradigms that push the frontiers of traditional matrix factorisation. It is driven by applications in song recommendation, text information retrieval, music signal processing, remote sensing, medical imaging.\"

Work performed

\"1) Binary and integer-valued matrix factorisation
Depending on the setting, the coefficients of the matrix data can be either continuous (real or complex-valued) or discrete (integer-valued, 1/0). The latter case concerns for example count data (song play-counts, word occurrences) or binary data (song played/unplayed, feature absent/present). It has been somehow less studied than the former and we developed and studied matrix factorisation techniques for binary and integer-valued data. In particular, we proposed new probabilistic models that account for the \"\"over-dispersion\"\" that is characteristic of some datasets, such as song play counts (some users are heavier listeners than others, some songs are much more popular than others). Regarding binary matrix factorisation, we proposed & studied probabilistic models that improve the interpretability of the estimated factors (using so-called \"\"mean-parametrisation\"\") as compared to more traditional approaches (that rely to a \"\"link function\"\").

2) Learning matrix-factorisation transforms
In many settings, the matrix data is a collection of features computed from raw data. For example, in signal processing, the spectrogram is a time-frequency transform of a temporal sequence. In text analysis, a so-called tf-idf transform is usually applied to the raw word counts in order to homogenise the data coefficients. The role of these transform is to produce salient features of the raw data, that in particular are amenable to factorisation (i.e., the factorisation of these features makes sense). These features are computed with off-the-shelf transforms that may set a limit to performance. We proposed a new paradigm in which an optimal factorising transform is learnt together with the factors in a single step. This led to promising results in audio signal processing settings, where we showed that the usual Fourier transform can be efficiently replaced by an adaptive short-term transform.

3) Multimodal data processing with joint matrix factorisation
Sometimes data is available in several ``modalities\'\'. If you take songs for example, you may have access to the audio together with the text lyrics. If these songs are part of a music streaming service, they may be available with ratings or play counts. All these sources share mutual information; lyrics are correlated with the type of music and songs may tell you about the people who listen to them (and people who listen to same songs are likely to get along). One way to model the information between the various data modalities, represented in matrix form, is to consider co-factorisation: the shared information is modelled by shared factors. We used this paradigm to improve song recommandation scores (by coupling play counts with music tags) and for audio-guided visual stream synthesis part an art/science collaboration.

4) Temporal matrix factorisation
Sometimes the data samples are heavily correlated. This occurs for example when dealing with spectrograms: the short-term spectra being computed over short periods of time, two adjacent spectra will \"\"look alike\"\". This correlation should be taken into account in the factorisation to produce more accurate decompositions. We are studying new probabilistic temporal models that smoothes the activation coefficients of the individual patterns to take this correlation into account.

5) Applications in imaging
We designed algorithms for applications of matrix factorisation in imaging settings, namely in dynamical PET imaging and remote sensing where the goal is to \"\"unmix\"\" the contributions of latent sources/components that explain the data. Regarding PET imaging in particular, we developed a generic algorithm for a large class of measures of fit that can better describe the acquisition model, resulting in more accurate identification of pathological areas in a neuro-imaging application.\"

Final results

As explained above, project FACTORY is revisiting and pushing the frontiers of traditional matrix factorisation. Besides the topics presented above, we have a number of activities starting in deep learning, and in particular uniting the potentials of deep learning (which is efficient at learning transforms) and matrix factorisation (which is efficient at analysing composite data). These activities are expected to take an important role in the second half of the project.