Date of Submission

7-2025

Date of Award

7-2025

Institute Name (Publisher)

Indian Statistical Institute

Document Type

Master's Dissertation

Degree Name

Master of Technology

Subject Name

Cryptology

Department

Cryptology and Security Research Unit (CSRU-Kolkata)

Supervisor

Bhattacharyya, Malay

Co-Supervisor (if any)

Mukhopadhyay, Anirban

Abstract (Summary of the Work)

Multiview learning aims to integrate diverse feature representations to achieve a comprehen- sive understanding of data. Traditional approaches often assume strict alignment across views, making them ill-suited for real-world scenarios where low-quality conflictive instances, i.e. in- stances with conflicting information across views are prevalent. Existing methods largely focus on eliminating conflicting instances by discarding them or substituting conflicting views, over- looking the need for practical decision making in such cases. Furthermore, while the recently proposed Reliable Conflictive Multiview Learning (RCML) framework introduces the idea of attaching reliabilities to decision outcomes, it leaves certain theoretical gaps unaddressed, es-pecially prioritization of conflictive views in fusion process in a principled manner.

Control Number

CrS2316

DOI

https://dspace.isical.ac.in/items/66689491-5581-484f-9cc8-41172a3ce1ab

DSpace Identifier

http://hdl.handle.net/10263/7607

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