Applications of Matrix completion theory to image processing (Record no. 6576)
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| 000 -LEADER | |
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| fixed length control field | 02101nam a22002057a 4500 |
| 003 - CONTROL NUMBER IDENTIFIER | |
| control field | OSt |
| 005 - DATE AND TIME OF LATEST TRANSACTION | |
| control field | 20220107122853.0 |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION | |
| fixed length control field | 190711b xxu||||| |||| 00| 0 eng d |
| 040 ## - CATALOGING SOURCE | |
| Transcribing agency | IIITMK |
| 100 ## - MAIN ENTRY--PERSONAL NAME | |
| Personal name | Vineetha K V (41718015) |
| 9 (RLIN) | 16202 |
| 245 ## - TITLE STATEMENT | |
| Title | Applications of Matrix completion theory to image processing |
| 300 ## - PHYSICAL DESCRIPTION | |
| Extent | MPhil CS 2018-2019 |
| 500 ## - GENERAL NOTE | |
| General note | Matrix completion is the problem of recovering a low rank matrix from partially observed entries. It has<br/>been widely used in collaborative filtering and recommender systems, dimension reduction and multiclass<br/>learning. This area is a recently emerged field of study following the track of what has been explored in<br/>fields related to compressed sensing. Like in compressed sensing, matrix completion algorithms, therefore,<br/>involve reconstruction of the data matrix from a small subset of its noise corrupted entries. The missing<br/>entries can be recovered under certain conditions when the data matrix has a low rank. The conditions<br/>mainly stipulate that the number of available entries fall below a certain limit; and that some of the rows<br/>and columns of the matrix are completely unknown. There has been extensive work on designing efficient<br/>algorithms for matrix completion with guarantees. Earlier works on matrix completion were based on<br/>convex relaxations. These algorithms achieve strong statistical guarantees, but are quite computationally<br/>expensive in practice. More recently, there has been growing interest in analyzing non-convex algorithms<br/>for matrix completion. These algorithms are much faster than the convex relaxation algorithms, which is<br/>crucial for their empirical success in large-scale collaborative filtering applications. We compare both<br/>convex and non convex approaches for Matrix Completion. The results are generalized to the setting when<br/>the observed entries contain noise. |
| 502 ## - DISSERTATION NOTE | |
| Degree type | MPhil CS |
| Name of granting institution | 2018-2019 |
| Year degree granted | INT |
| -- | Dr Joseph Suresh Paul |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical term or geographic name entry element | Matrix Completion |
| 9 (RLIN) | 16203 |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical term or geographic name entry element | Data Matrix |
| 9 (RLIN) | 16204 |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical term or geographic name entry element | Low Rank Matrix |
| 9 (RLIN) | 16205 |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical term or geographic name entry element | Image Processing |
| 9 (RLIN) | 16206 |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) | |
| Source of classification or shelving scheme | Dewey Decimal Classification |
| Koha item type | |
| Withdrawn status | Lost status | Source of classification or shelving scheme | Damaged status | Not for loan | Collection code | Home library | Current library | Shelving location | Date acquired | Total Checkouts | Barcode | Date last seen | Price effective from | Koha item type |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Dewey Decimal Classification | Non Fiction | IIITM-K | Kerala University of Digital Sciences, Innovation and Technology Knowledge Centre | 11/07/2019 | R-1580 | 11/07/2019 | 11/07/2019 | Project Reports |