106 research outputs found
Multilinear Subspace Clustering
In this paper we present a new model and an algorithm for unsupervised
clustering of 2-D data such as images. We assume that the data comes from a
union of multilinear subspaces (UOMS) model, which is a specific structured
case of the much studied union of subspaces (UOS) model. For segmentation under
this model, we develop Multilinear Subspace Clustering (MSC) algorithm and
evaluate its performance on the YaleB and Olivietti image data sets. We show
that MSC is highly competitive with existing algorithms employing the UOS model
in terms of clustering performance while enjoying improvement in computational
complexity
Learning, Probability and Logic: Toward a Unified Approach for Content-Based Music Information Retrieval
Within the last 15 years, the field of Music Information Retrieval (MIR) has made tremendous progress in the development of algorithms for organizing and analyzing the ever-increasing large and varied amount of music and music-related data available digitally. However, the development of content-based methods to enable or ameliorate multimedia retrieval still remains a central challenge. In this perspective paper, we critically look at the problem of automatic chord estimation from audio recordings as a case study of content-based algorithms, and point out several bottlenecks in current approaches: expressiveness and flexibility are obtained to the expense of robustness and vice versa; available multimodal sources of information are little exploited; modeling multi-faceted and strongly interrelated musical information is limited with current architectures; models are typically restricted to short-term analysis that does not account for the hierarchical temporal structure of musical signals. Dealing with music data requires the ability to tackle both uncertainty and complex relational structure at multiple levels of representation. Traditional approaches have generally treated these two aspects separately, probability and learning being the usual way to represent uncertainty in knowledge, while logical representation being the usual way to represent knowledge and complex relational information. We advocate that the identified hurdles of current approaches could be overcome by recent developments in the area of Statistical Relational Artificial Intelligence (StarAI) that unifies probability, logic and (deep) learning. We show that existing approaches used in MIR find powerful extensions and unifications in StarAI, and we explain why we think it is time to consider the new perspectives offered by this promising research field
ekernf01/knockoffs_ecoli: v0.0.1
<p>Initial release accompanying revision of our manuscript, under review circa November 2023.</p>
ekernf01/knockoffs_quick_demo: v0.0.1
<p>Initial release accompanying revision of our manuscript, under review circa November 2023.</p>
ekernf01/knockoffs_shareseq: v0.0.1
<p>Initial release accompanying revision of our manuscript, under review circa November 2023.</p>
ekernf01/rlookc: v0.0.1
<p>Initial release accompanying revision of our manuscript, under review circa November 2023.</p>
ekernf01/pylookc: v0.0.1
<p>Initial release accompanying revision of our manuscript, under review circa November 2023.</p>
- …